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Hire remote Engineering Manager

Discover developers that match your project requirements.

No exact match for this specialty yet — here are related experts from our network.

Aditya P.
Available Now
Verified in SoftDoesAditya P.
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Aditya P.
Available Now
Aditya P.Verified in SoftDoes
DevOps Engineer/ Site Reliability Engineer
US 🇺🇸English (B2)Senior
GoPythonUNIX Shell ScriptingPHP

Principal DevOps / Site Reliability Engineer with 14+ years of software engineering experience (including freelance development) and 9+ years of professional DevOps/SRE experience. Expert in designing and automating scalable cloud infrastructure across AWS, GCP, Azure, a company Cloud, with deep expertise in Kubernetes, Docker, OpenShift, Terraform, Ansible, GitOps (ArgoCD/FluxCD), CI/CD, Go, and Python. Experienced in building multi-cloud, high-availability platforms, infrastructure automation, cloud migrations, and developer platforms. Currently working as Principal Engineer at FOX, previously held senior engineering roles at Hippo Insurance, a company, and Morgan Stanley.

Andrea M.
Available Now
Verified in SoftDoesAndrea M.
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrea M.
Available Now
Andrea M.Verified in SoftDoes
AI Solutions Architect/Engineer
IT 🇮🇹English (C1)Senior
PythonLangChainLangGraphGoogle Cloud

I am a solutions architect and engineer with 5+ years of experience in designing and implementing cloud-based AI solutions. Solid skills across applied AI, software engineering, cloud architecture and DevOps practices, built on years of field experience, allow me to drive AI initiatives from concept to production, leveraging AI tools to boost delivery speed without compromising on deliverable quality.

Andrii V.
Available Now
Verified in SoftDoesAndrii V.
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Andrii V.
Available Now
Andrii V.Verified in SoftDoes
Senior Python Engineer
CA 🇨🇦English (C1)Senior
PythonDjangoFastAPIFlask

Senior Python Developer with 11+ years of experience designing scalable backend systems and microservice architectures across data analytics, logistics, and fintech platforms. Proven track record leading engineering teams and delivering cloud-native solutions on AWS and Kubernetes. Recently led development of AI-powered and real-time observability platforms using modern Python frameworks and large-scale data technologies. Strong background in system architecture, distributed systems, and mentoring teams in Agile environments.

Boris S.
Available Now
Verified in SoftDoesBoris S.
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Boris S.
Available Now
Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
PythonSQLBashFastAPI

Senior AI/ML Engineer with 10+ years of experience designing and deploying large-scale machine learning and generative AI systems in production environments. Specialized in LLM architectures, agentic AI systems, distributed ML infrastructure, and end-to-end MLOps platforms. Proven track record of delivering high-impact AI products including diagnostic AI systems improving accuracy by 38% and enterprise ML platforms accelerating data pipelines by 40%. Experienced in leading cross-functional teams, architecting scalable AI platforms, and driving AI innovation across healthcare, enterprise analytics, and automation domains.

Eugene M.
Available Now
Verified in SoftDoesEugene M.
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
Available Now
Eugene M.Verified in SoftDoes
DevOps Engineer
ES 🇪🇸English (C2)Senior
AWSGoogle CloudKubernetesTerraform

10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Hripsime S.
Available Now
Verified in SoftDoesHripsime S.
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Hripsime S.
Available Now
Hripsime S.Verified in SoftDoes
AI Engineer | Data Scientist
FR 🇫🇷English (C1)Senior
Deep LearningMachine LearningInterpretabilityConvolutional Neural Networks

AI engineer with 6+ years of professional experience, available to start a new challenging role in stimulating and innovative company.

Mario J.
Available Now
Verified in SoftDoesMario J.
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Mario J.
Available Now
Mario J.Verified in SoftDoes
Senior Data Engineer
GT 🇬🇹English (C1)Senior
PythonSQLPandasPySpark

Over the past 8 years, I have gained ample experience in ETL/ELT, Snowflake, Airflow, and Data Warehouse. I have been responsible for Data Lake Support and creating new ETL/ELT processes within AWS using either Snowflake, Glue, Airflow, dbt, Xplenty, Docker, or Custom Python Scripts with APIs. I have also worked as an Application Architect, which includes over 50 different services across AWS. I needed to create pipelines, organizations, and permissions within AWS Infrastructure to meet business requirements while optimizing costs. My previous work includes over four years as a Data Analyst, creating and supporting BI Tools like Sisense, Quicksight, and Power BI/Tableau. I managed and created dashboards, widgets, and Data Architecture with Sisense and Redshift. I was also the Support Engineer for Sisense Installation and Maintenance. I would describe myself as reliable and able to undertake complex situations with unique solutions. I have over 7 years of experience in the Data sector, over 17+ years of SQL experience, and 20+ years as a Developer. I’m also a mid-QA Automation Engineer, which gives me a better idea of the attention to detail a developer needs to attain to complete development within the time limit and without affecting PROD releases. My other skills include being a Sysadmin in both Windows and Linux environments, a company Web Services AWS, a company 365 Portal, JumpCloud, 1Password, and Slack. I also have DBA knowledge, including creating servers and Data Warehouses. I'm an expert in MS Office and can automate with macros. I’m also a Cybersecurity advocate since I’m always looking for ways to protect data and user permissions. I have been in Agile Development with Jira in Kanban and Scrum for the past 4 years. And I have also created bots to automatize process alerts and configurations with Slack APIs and AWS to help my teammates get prompt feedback from the ETL pipelines.

Raphael O.
Available Now
Verified in SoftDoesRaphael O.
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Raphael O.
Available Now
Raphael O.Verified in SoftDoes
Snr. Staff Software Engineer
US 🇺🇸English (Native)Senior
LeadershipAgileBig dataAPI design

I am a technical leader and problem solver who designs and delivers scalable, distributed software and AI systems that create measurable business value. I have led cross-functional teams and platform initiatives, translating complex requirements into reliable, production-ready solutions built on open standards. My experience spans architecting, mentoring, and driving execution across diverse technology stacks, with a strong focus on operational excellence, product impact, and long-term system sustainability.

Santiago G.
Available Now
Verified in SoftDoesSantiago G.
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Santiago G.
Available Now
Santiago G.Verified in SoftDoes
DevOps Engineer
US 🇺🇸English (C1)Senior
LinuxBashDockerAnsible

Energetic, adaptable, mission-focused and multilingual MBA professional with more than 20 years of experience in IT, Business Intelligence, Marketing and Finance in 4 multinationals. Proven track record of finding creative solutions to solving challenges. Effective presenter who collaborates well in teams and across divisions. Passion for learning and acquiring new skills every day.

Thierry M.
Available Now
Verified in SoftDoesThierry M.
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thierry M.
Available Now
Thierry M.Verified in SoftDoes
Senior Infrastructure / Production Engineer
US 🇺🇸English (C1)Senior
AWSGoogle CloudAzureKubernetes

15+ years of experience designing and operating large-scale distributed systems and latency-sensitive production platforms. Expertise in multi-region Kubernetes infrastructure, hybrid cloud environments, and production reliability engineering, with a strong focus on scalability, fault tolerance, observability, and operational automation. Deep experience building and operating hybrid bare-metal and cloud platforms (AWS, GCP), including Kubernetes infrastructure, traffic routing, CI/CD systems, and production observability stacks supporting globally distributed workloads. Strong background in incident response, Linux systems, networking, distributed systems troubleshooting, and infrastructure automation using Terraform and Python. Focused on end-to-end ownership of production infrastructure platforms while partnering closely with engineering teams to improve reliability, security, deployment consistency, and operational efficiency.

Thomas S.
Available Now
Verified in SoftDoesThomas S.
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Thomas S.
Available Now
Thomas S.Verified in SoftDoes
Technical leader at a company AI Core
DE 🇩🇪English (C1)Team Lead
LeadershipMentorshipGenerative AIPyTorch

Technical leader at a company AI Core, building and operating cloud-native AI platform services from prototype to production. Hands-on across backend engineering, distributed systems, and Kubernetes, driving technical direction and delivery across teams without direct authority.

Tzechung K.
Available Now
Verified in SoftDoesTzechung K.
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

Tzechung K.
Available Now
Tzechung K.Verified in SoftDoes
Lead AI/ML Developer
US 🇺🇸English (Native)Senior
Claude CodeOpenAI CodexGoogle AntigravityLLM Fine-Tuning

Software Engineer with 10+ years of AI/ML experience and proven expertise in developing LLM-powered applications, generative AI systems, and ad engines. Strong track record of scaling ML platforms from inception to 1,500+ users at a company, building production AI systems, and creating innovative ad solutions. Experienced in full software development lifecycle with deep knowledge of PyTorch, TensorFlow, and modern AI frameworks including LangChain, LangGraph, and various LLM APIs.

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What our Engineering Managers can build

Not sure which engagement model fits?

SoftDoes takes full ownership of delivery, combining project management, engineering, design, and QA into one accountable team focused on successful outcomes.

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RIGHT expert, FASTER

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How to hire a Engineering Manager

01
BROWSE PROFILESRIGHT NOW

Fill out a short form and see who's on the bench. Real profiles, verified histories.

02
Interview1-3 DAYS

Tell us what you need. We propose two or three candidates from the bench; you interview them directly.

03
OnboardWEEK ONE

Your engineer starts on your project. Contract, payments, and the guarantee run through us.

US VS. THE DATABASE

Time to Start
Talent Quality
Technical Vetting
Flexibility
Operational Overhead
Cost Efficiency
cursor
<SoftDoes>
Time to Start
1-2 weeks
Talent Quality
Senior-only engineers
Technical Vetting
Multi-stage screening
Flexibility
Scale up or down anytime
Operational Overhead
As managed as you want
Cost Efficiency
Competitive, fee-free
Talent Marketplaces
Time to Start
1-3 months
Talent Quality
Mixed experience levels
Technical Vetting
One screen, then gone
Flexibility
Contract restrictions
Operational Overhead
Partially managed
Cost Efficiency
Agency markup
In-House Hiring
Time to Start
2-6 months
Talent Quality
Depends on market
Technical Vetting
Internal responsibility
Flexibility
Long-term commitment
Operational Overhead
Fully internal
Cost Efficiency
Highest total cost

Frequently Asked Questions

Everything you need to know about deploying, scaling, and securing your neural agents with SoftDoes. Can’t find an answer?

How long does it take to hire an Engineering Manager through SoftDoes?

For most engagements, SoftDoes can match you with a qualified engineering manager within two to three weeks from initial role definition to offer acceptance. If you need fractional engineering leadership, the timeline is even faster, often as little as three days. More specialized requirements, such as deep domain expertise in regulated industries or experience with managed attribution browser technologies, may extend the process slightly. The key advantage is that candidates in our network are pre vetted, which eliminates the months of sourcing and screening that typically slow down engineering manager hiring.

What does it cost to hire an Engineering Manager?

Costs depend on the engagement model and seniority level. Full time engineering managers earn between $150K to $350K annually, with variation based on geography, industry, and scope. Fractional engineering managers start at $5,000 per month, offering a cost effective way to access engineering leadership without full time overhead. Interim engineering managers, ideal for bridging gaps during transitions or scaling periods, cost around $22,400 per month. Beyond salary, factor in the cost of recruiting time, interview cycles, and the opportunity cost of delayed delivery. Working with a vetted talent partner significantly reduces these hidden costs.

What engagement models are available (dedicated hire, pod, contract)?

SoftDoes offers multiple engagement models to match your situation. You can hire a dedicated full time engineering manager who integrates completely with your organization. For companies not ready for a full time commitment, fractional or contract engineering managers provide leadership on a part time basis. The pod model pairs an engineering manager with senior engineers and specialists for complete team delivery on large scale technical initiatives. You can also start with one model and transition as your needs evolve, moving from fractional to full time or expanding from a single hire to a full engineering team.

How do you ensure time zone alignment with an Engineering Manager?

SoftDoes focuses on North American talent, which naturally supports time zone alignment for US and Canadian companies. During the vetting process, we assess each candidate's location, availability, and experience working with distributed teams. We establish clear expectations around overlap windows, meeting times, and async communication norms. For teams with global distribution, we prioritize candidates who have proven experience coordinating across time zones using structured async practices, ensuring that production operations and delivery cadence are never disrupted by geography.

How does SoftDoes technically vet an Engineering Manager?

Our vetting process uses a structured scorecard across multiple dimensions: technical credibility, people leadership, delivery and prioritization, and cross functional judgment. Candidates go through behavioral interviews focused on real scenarios such as coaching underperformers, navigating architectural trade offs, and coordinating cross team dependencies. We evaluate technical depth through design document reviews and system architecture discussions rather than abstract coding puzzles. Reference checks with former direct reports and peers validate leadership claims. This multi dimensional approach ensures that every engineering manager we present can both provide technical leadership and build high performing teams.

What happens if the Engineering Manager isn't the right fit, or I need to scale up or down?

SoftDoes includes replacement guarantees in its engagement models. If an engineering manager is not meeting expectations after the initial evaluation period, we work with you to identify the mismatch and provide a replacement without additional recruiting costs. Early review checkpoints, typically at the 90 day mark, help catch fit issues before they become costly. If your needs change and you need to scale up by adding more engineers or leadership, or scale down as a project phase concludes, SoftDoes offers the flexibility to adjust your engagement without long term contractual constraints.

How to Hire an Engineering Manager

Most companies lose weeks chasing the wrong engineering manager candidates, burning budget on misaligned hires who stall delivery instead of accelerating it. The difference between a great EM hire and a mediocre one shows up fast: in missed deadlines, rising attrition, and teams that never gel. This guide walks you through exactly how to define the role, prepare internally, source and vet candidates, spot warning signs, and onboard effectively so your next engineering manager hire drives real business outcomes from day one.

What an Engineering Manager Actually Does (and Why Most Companies Get It Wrong)

The Real Day to Day of an Engineering Manager

An engineering manager is not simply a senior developer who got promoted into a people role. Engineering managers lead teams of engineers to deliver projects, but their core job is making the team produce more than the sum of its parts. That means shifting focus from personal technical contributions to people, process, delivery, and strategic alignment.

An engineering manager does not need to be the best coder but should understand architecture and trade offs. Their technical judgment includes understanding system architecture and making decisions without micromanaging. They must be able to connect daily tasks to broader company goals, translating high level business objectives into concrete engineering work.

Here is what the role looks like in practice:

  • One on one coaching and performance management: Regular sessions with direct reports to identify blockers, mentor engineers, manage underperformers, and develop engineers toward promotion readiness. Feedback delivery involves giving constructive and timely criticism to team members.
  • Delivery planning and prioritization: Guiding sprint planning, balancing product delivery against technical debt, and making scope and trade off decisions. They estimate delivery timelines and manage project timelines to ensure quality standards are met.
  • Technical oversight and architectural guidance: Reviewing design proposals, evaluating trade offs around scalability and reliability, and driving architectural decisions. A hands on engineering manager maintains enough technical credibility to provide technical leadership without writing production code full time.
  • Hiring, onboarding, and team building: Defining role needs, running interviews, shaping team composition. Engineering managers can lead teams of up to 12 engineers, and they hire and coach engineers as part of building high performing groups.
  • Cross functional collaboration: Engineering managers collaborate with product and design teams, working alongside UX, Security, and Compliance stakeholders to coordinate cross functional delivery and ensure technical work supports business objectives.
  • Team health and culture: Setting norms for code reviews, deploy processes, incident response. Effective engineering managers should create an environment of psychological safety for their teams, set team culture, and drive process improvements for team efficiency.

Engineering manager roles can differ significantly based on team stage and specific challenges. A full time engineering manager at a scale up building scalable cloud native systems faces very different demands than one leading a product engineering team modernizing legacy infrastructure in a regulated industry.

Why This Hire Is a Strategic Priority, Not Just a Backfill

Hiring the right engineering manager is one of the highest leverage decisions a CTO or VP of Engineering can make. The business impact is measurable:

  • Faster, more predictable delivery: A strong EM stabilizes cycle times, improves commitment accuracy, and helps the team ship reliable software on schedule. When delivery becomes predictable, product development planning across the organization gets dramatically easier.
  • Lower costs and reduced technical debt: Good engineering managers balance product delivery with long term system health, preventing the kind of accumulated debt that leads to expensive rework, support overhead, and reliability failures. They ensure fault tolerant systems and reliable data platforms stay a priority even under feature pressure.
  • Higher retention and stronger talent development: Engineering managers who mentor engineers, provide growth paths, and foster operational excellence reduce voluntary attrition. Healthy engineering orgs target less than 10% annual voluntary turnover. The EM's ability to develop engineers is directly tied to whether your best people stay or leave.
  • Scalable growth and compliance readiness: In regulated industries like healthcare and finance, an engineering manager ensures the team understands healthcare data standards, operational and compliance standards, security verification requirements, and audit readiness. They enable scaling by adding teams, handling distributed teams, and internalizing processes that support growth.

A successful engineering manager balances management with technical contributions, which means they provide technical guidance on system design while also owning people outcomes like retention, promotion velocity, and team morale.

How to Prepare Before You Open the Role

Defining What You Actually Need

Companies should define what the specific team needs before hiring an engineering manager. Skipping this step leads to vague job descriptions, misaligned candidates, and wasted interview cycles. Here is how to get clear internally before you post.

Project Scope and Requirements

Start by defining what the EM will own. Are they leading a team developing web applications from scratch, modernizing a legacy monolith, integrating AI/ML pipelines, or managing a search engineering team building a high throughput search platform? The scope determines the technical depth required.

If your company needs someone to champion ai forward automation or operate scalable data collection systems, say so explicitly. If the role involves performing security verification or building for national security customers, those constraints shape everything from candidate profiles to clearance requirements.

Team Structure and Engagement Model

Determine whether this EM will manage individual contributors or also lead other managers. How many direct reports? Will they coordinate cross team dependencies across multiple squads? Is this a full stack engineering team or a specialized data foundations engineering team?

Clarify reporting lines: does the EM report to a VP of Engineering, a CTO, or a founder? What cross functional teams will they partner with? How much existing process and structure is already in place versus needing to be built?

In House vs. Dedicated Remote Talent

Remote and hybrid engineering leadership is increasingly standard, especially for teams building distributed systems or working across time zones. But the decision between in house and dedicated remote talent involves real trade offs.

Remote EMs offer cost flexibility and access to a broader talent pool. In regulated industries, however, you need to evaluate data governance, IP protections, and time zone overlap carefully. If you need a custom software development partner who can embed senior leadership into your team remotely, the engagement model matters as much as the candidate's resume.

Writing a Job Description That Attracts the Right Candidates

A standout job description for engineering manager jobs must go beyond a generic list of responsibilities. It should cover four key elements:

  • Mission and expectations: Why does this role exist? What business outcomes will the EM drive? Be specific: "Lead the targeted collection engineering team to deliver scalable enterprise software for our core enterprise product development initiative" is far more compelling than "manage a team of engineers."
  • Technical stack and context: What technologies, architectures, and systems will the EM interact with? Mention cloud providers, languages, frameworks, and any legacy constraints. If the team is focused on agile api management apis or building search capabilities, say so. Include regulatory or security constraints relevant to the role.
  • Team structure and reporting: Number of direct reports, whether the role includes managing managers, remote or hybrid setup, and which functions they will partner with. A technical team focused on mobile development has different dynamics than a frontend platform team or a team building backend services.
  • Growth, impact, and career path: What trajectory does this role offer? Will the EM have the opportunity to set technical direction for a growing organization, scale a team, or deliver platform improvements that shape the product roadmap? Candidates for top engineering manager jobs want to see where the role leads.

Include salary range transparency. Full time engineering managers earn between $150K to $350K annually depending on seniority, geography, and domain. Senior engineering managers in Boston earn between $170K to $270K annually, while engineering managers in Los Angeles start at $150K annually. Being upfront about compensation filters out mismatches early.

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How to Source, Vet, and Onboard Engineering Managers

Building a Hiring Process That Actually Works

Effective engineering management requires different competencies than technical excellence. Your hiring process must test for both leadership maturity and technical credibility, not just one or the other.

Sourcing Strategy

The best engineering managers rarely respond to job board postings. They are already employed, performing well, and selectively evaluating their next move. Your sourcing approach should reflect this reality.

  • Referrals and leadership networks: Tap your existing engineering leadership for warm introductions. Engineering leadership communities, Slack groups, and industry meetups are rich sources of passive candidates.
  • Vetted talent networks: Working with a partner who maintains a pre screened pool of senior engineering talent dramatically reduces time to hire. SoftDoes maintains a talent network of carefully vetted engineering managers and senior engineers, which means you skip the months of outbound sourcing and resume screening.
  • Outbound executive search: For specialized roles requiring deep domain expertise (AI/ML, compliance, security service backgrounds), targeted outbound recruiting may be necessary.

Trade offs are real: passive and referral pipelines yield higher quality but move slower. Job boards scale reach but attract significant noise. The fastest path to a qualified engineering manager is often through a vetted network where candidates have already been screened for technical credibility, people leadership, and professional services experience.

Vetting Beyond the Resume

Effective engineering managers should use structured interview loops testing people leadership and system execution. Here is a recommended process:

  1. Recruiter screen: Assess motivations, management span, remote work comfort, compensation expectations. Filter for basic alignment before investing team time.
  2. Behavioral interviews (people leadership): Candidates should demonstrate experience in coaching, performance management, and resolving conflict. Use structured questions with anchored scoring rubrics. Assessing how candidates handle underperformance is important. Behavioral scenarios and situational questions help evaluate a candidate's potential in managing technical teams.
  3. Technical deep dive: Not a coding test. Instead, review real design documents, evaluate architectural trade offs, and discuss distributed systems, scalability, and production operations. Candidates for engineering manager roles should possess enough technical context to earn team respect.
  4. Cross functional partner interview: Hiring managers should involve peers and cross functional partners in the interview process. Have Product, Design, or Compliance stakeholders evaluate the candidate's ability to translate technical trade offs into business outcomes and coordinate cross team dependencies.
  5. Reference checks: Speak with former direct reports and peers. Ask about how the candidate drove team development, handled real failures, and delivered under pressure. Evaluating mentorship and talent growth is crucial when hiring an engineering manager.

Evaluating a candidate's ability to communicate effectively is vital for an engineering manager. Throughout every stage, assess clarity of communication, comfort with ambiguity, and alignment with your team culture. An engineering manager must adapt their leadership style to the team culture and autonomy level already in place.

Use a clear scoring rubric across dimensions: technical credibility, people leadership, delivery and prioritization, cross functional judgment, and fostering technical rigor. Keep the loop to three or four rounds and aim to extend an offer within two weeks. Slow processes lose strong candidates who have multiple options.

Setting Up the First 90 Days for Success

Onboarding an engineering manager well is as important as hiring one. A structured 30/60/90 day plan prevents the common failure mode where a new EM floats for months without clear ownership.

Days 1 to 30 (Orientation):

  • Introduce team members, stakeholders, and cross functional partners
  • Deep dive into product, architecture, existing codebase, and business intelligence systems
  • Observe existing meetings, rituals, and quality practices
  • Understand compliance context, security requirements, and data reliability standards
  • Achieve small wins through low risk tasks that build credibility

Days 31 to 60 (Contribution):

  • Begin leading parts of projects and taking ownership of deliverables
  • Propose process improvements based on observations
  • Start regular one on ones with all direct reports
  • Build trust and own engagement relationships across teams
  • Coordinate cross functional delivery on at least one active initiative

Days 61 to 90 (Independence):

  • Manage roadmap, set technical strategy, and define team goals
  • Drive performance improvements and ship customer facing features
  • Execute change on identified bottlenecks or process gaps
  • Mentor engineers and manage performance with confidence
  • Deliver customer facing reporting on team health and delivery metrics

Successful engineering managers should empower their teams while avoiding micromanagement. By day 90, the EM should be operating independently, setting technical direction, and delivering measurable impact. Measure onboarding success by feedback from direct reports, time to first production deploy, and clarity of role ownership.

Retention starts on day one. Clear career paths, psychological safety, transparent feedback loops, and alignment with company mission keep engineering managers engaged long term. Without these, even a great hire will start looking at other opportunities within a year.

How to Spot the Right (and Wrong) Candidate

Red Flags and Green Flags in Engineering Manager Interviews

Hiring engineering managers requires balancing technical competence with leadership maturity. Here are the signals that separate strong candidates from risky ones.

Red Flags:

  • All theory, no specifics. The candidate speaks in hypotheticals about management philosophy but cannot describe a real situation where they coached an underperformer, navigated a conflict, or made a difficult trade off. No concrete stories means no proven track record.
  • Deflects technical questions entirely. Saying "we have an architect for that" signals a lack of the technical judgment needed to provide technical leadership, set technical direction, or earn credibility with a technical team.
  • No evidence of growing people. If no one has been promoted, mentored, or developed under their leadership, they are likely managing tasks rather than building capability. Engineering managers should mentor engineers effectively, and that should show in results.
  • Overemphasis on personal contribution. A candidate who talks primarily about their own code, their own designs, or their own heroics is signaling that they operate as an individual contributor, not a multiplier. Engineering managers drive outcomes through the engineering team, not around it.

Green Flags:

  • Concrete examples of people development with outcomes. They can describe how they helped engineers grow, handled underperformance with fairness and clarity, and built team culture that retained top talent. They balance product delivery with long term talent investment.
  • Strong technical judgment without micromanagement. They review design documents, spot architectural risks, and understand trade offs around scalability, reliability, and cost. They set technical strategy and provide technical guidance without writing every line of code.
  • Evidence of building and scaling teams. They have hired, onboarded, and structured engineering teams. They understand how to coach engineers, coordinate cross team dependencies, and scale from a small squad to multiple teams. They can describe how they deliver scalable enterprise software through team structure rather than individual heroics.
  • Cross functional fluency. They translate technical concepts for non technical stakeholders, negotiate scope with Product, partner with Security and Compliance, and coordinate cross functional delivery naturally. They have experience with product led growth strategies and understand how engineering supports business outcomes.

Why Partnering with SoftDoes Gives You an Edge

Finding an engineering manager who combines technical credibility, people leadership, and domain expertise is hard. Doing it quickly without compromising quality is even harder.

SoftDoes is a North America focused software engineering and talent delivery partner serving clients across the US and Canada. When you work with SoftDoes, you get access to carefully vetted engineering managers who have already been screened for technical depth, leadership maturity, and the ability to ship reliable software in complex environments.

What makes the partnership different:

  • Pre vetted senior talent. Every engineering manager candidate in our talent network has been evaluated across technical credibility, people leadership, delivery track record, and cross functional judgment. With a 98% match success rate for engineering manager hires, you spend less time interviewing and more time building.
  • Team delivery model. SoftDoes does not provide isolated freelancers. You get engineering leadership embedded in a team delivery approach, whether that means a single dedicated EM, an EM paired with senior engineers, or a full pod model for large scale initiatives.
  • Flexible engagement models. Fractional engineering managers provide leadership without full time overhead, starting at $5,000 per month. Interim engineering managers cost around $22,400 per month for intensive engagements. Full time embedded managers integrate completely with your organization. Scale up or down as your needs change.
  • Replacement guarantees and scaling options. If the fit is not right, SoftDoes provides replacement options without additional recruiting costs. As your product engineering team grows, you can expand from a single hire to a full engineering leadership structure.

Hiring a fractional engineering manager takes about 3 days through SoftDoes, compared to the weeks or months typical of traditional recruiting. For companies building reliable data platforms, managing production operations at scale, or navigating complex operational security web scraping and compliance requirements, this speed matters.

Ready to Hire an Engineering Manager?

Stop losing momentum to open leadership gaps. Whether you need a hands on engineering manager for a growing team or senior engineering leadership for a complex technical initiative, the next step is straightforward.

Schedule a discovery call with SoftDoes. We will assess your team structure, technical requirements, and business goals, then match you with a vetted engineering manager who fits your stack, your culture, and your timeline.

No long term lock in. No wasted interview cycles. Just qualified engineering leadership, ready to deliver.

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