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Aditya P.
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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.
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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.
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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.
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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.
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Andrii V.Verified in SoftDoes
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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.
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Verified in SoftDoesBoris S.
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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.
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Boris S.Verified in SoftDoes
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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.
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Boris S.Verified in SoftDoes
Senior AI/ML Engineer
BG 🇧🇬English (B2)Senior
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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.
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Verified in SoftDoesEugene M.
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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.
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Eugene M.Verified in SoftDoes
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ES 🇪🇸English (C2)Senior
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10+ years in IT, 7+ years focused on DevOps/SysOps/SRE. Currently - Tech Lead at a U.S. company.

Eugene M.
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Eugene M.Verified in SoftDoes
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AWSGoogle CloudKubernetesTerraform

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

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

Mario J.
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Verified in SoftDoesMario J.
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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.
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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.
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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.
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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 Investment Thesis Consultants can build

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How to hire a Investment Thesis Consultant

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 Investment Thesis Consultant through SoftDoes?

SoftDoes maintains a prescreened network of senior engineers and technical strategy consultants with deep domain experience across regulated industries, which compresses the typical 60 to 90 day search timeline. After an initial scoping session to define the core outcome, technical stack reality, and decision making authority, SoftDoes sources and presents qualified candidates within days rather than months. The exact timeline depends on the specificity of your requirements and the complexity of the engagement, but most clients move from scoping to active engagement faster than through traditional recruitment channels.

What does it cost to hire an Investment Thesis Consultant?

Consultant rates for senior technical strategy roles typically run 1.5x to 3x the cost of internal senior engineering leadership. That premium reflects the speed of deployment, the depth of domain expertise, and the reduced risk of a bad hire. SoftDoes structures pricing around outcomes and deliverables rather than hourly billing alone, so the cost aligns with measurable value creation. Engagement fees vary based on the scope (fractional advisory versus full time embedded), the duration, and the complexity of the investment thesis work. Ask for performance KPIs tied to cost savings, latency improvement, or incident reduction to ensure the investment pays for itself.

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

SoftDoes offers dedicated hire (full time embedded consultant), fractional retainer (part time advisory for ongoing strategy support), and project or phase based contracts (scoped to a specific deliverable like pre acquisition diligence or architecture modernization). A pod model is also available for engagements that require multiple specialists collaborating on a single thesis. The right model depends on whether you need someone for a defined 90 day value inflection period, a long term architecture leadership role, or an advisory presence across multiple deals and portfolio companies.

How do you ensure time zone alignment with an Investment Thesis Consultant?

SoftDoes focuses on North American delivery, sourcing consultants across the US and Canada to ensure core business hour overlap with your team. For remote engagements, SoftDoes establishes communication flow expectations and core overlap windows during the scoping phase, so collaboration cadence is defined before the engagement begins. This is not a best effort arrangement; time zone alignment is treated as a nonnegotiable operational requirement.

How does SoftDoes technically vet an Investment Thesis Consultant?

Vetting goes beyond resume review. SoftDoes uses a multi stage technical evaluation pipeline that includes live problem solving using real architecture scenarios, analysis of existing system diagrams to test tradeoff reasoning, communication exercises simulating nontechnical stakeholder interactions, and cross functional culture fit interviews with product, operations, and compliance leads. Senior engineers within SoftDoes evaluate each candidate's domain experience, portfolio of prior engagements, and references from similar industries. The goal is to confirm the consultant can operate under ambiguity, own nonfunctional requirements, and deliver measurable results within the first 90 days.

What happens if the Investment Thesis Consultant isn't the right fit, or I need to scale up or down?

SoftDoes provides a zero risk replacement guarantee. If the consultant does not meet your standards or the engagement needs shift, SoftDoes provides an immediate replacement at no additional cost. Scaling up or down is built into the engagement structure; you can move from a single advisory consultant to a full team, or reduce scope as the investment thesis matures, without renegotiating contracts from scratch. Clear exit points and milestone based reviews ensure you are never locked into an engagement that is not delivering value.

The Executive Guide to Hiring an Investment Thesis Consultant

A misaligned investment thesis consultant burns more than their fee; they burn months of engineering runway, delay capital raises, and leave your architecture exposed during diligence. The right placement pays for itself before the first invoice clears. This playbook gives you a field tested strategy to define, vet, and onboard top tier investment thesis consultant talent, built from years of placing senior technical strategy leaders into high stakes engagements.

What Is Actually at Stake When You Hire Wrong

Daily Realities That Separate Senior Investment Thesis Consultants from Task Executors

Most firms think they need a consultant who can review a tech stack and write a memo. That is an order taker. A senior investment thesis consultant operates as a cross functional leader who owns outcomes, not deliverables. Here is what that looks like in practice:

  • Architectural visioning and tradeoff management. They decide when to invest in platform refactoring versus shipping features, and they model the cost of each path against your thesis timeline. This is not theoretical; it determines whether your target company can sustain projected volume growth after close.
  • Ownership of nonfunctional requirements. Reliability, security, compliance, observability, and testability fall under their remit. They ensure engineering decisions align with regulatory and audit requirements across life sciences, finance, manufacturing, and other regulated verticals.
  • System design under real constraints and budget sensitivity. They model cloud infrastructure costs, data pipeline overhead, and AI inference spend. They identify hidden costs like technical debt maintenance, operations burden, and vendor lock in that generic consultants overlook.
  • Strategic alignment with business outcomes. Engineering roadmaps map directly to revenue drivers, risk mitigation, and market expansion. A senior consultant translates technical backlogs into investment thesis signals that private equity leadership and investment banking teams can act on.
  • Risk evaluation and active mitigation. System complexity, vendor lock in, AI model risk, data quality gaps, and latency constraints are not items on a checklist. They are threats that require designed mitigations, governance structures, and clear ownership.
  • Communication under ambiguity. They present to boards, investors, and nontechnical executives. Effective communication is necessary for translating complex data into clear narratives that support investment committee decisions. They surface failures early rather than hiding behind status reports.

Industry knowledge is crucial for consultants to understand market specific dynamics. A consultant who has done diligence on M&A transactions exceeding $1 billion brings pattern recognition that a generalist cannot replicate.

Financial and Operational Returns That Justify the Spend

The business case for hiring an investment thesis consultant rests on four ROI vectors you can measure:

  • Technical debt reduction as cost avoidance. Reducing rework, eliminating performance bottlenecks, and improving maintainability translate directly to lower outage costs, faster feature delivery, and reduced attrition from engineering burnout.
  • Faster deployment cycles. Compressing the path from ideation to production means earlier revenue capture and earlier competitive advantage. For enterprise clients, a single quarter of delay can cost seven figures in lost opportunity.
  • Infrastructure optimization. Cloud cost rightsizing, autoscaling, and efficient data pipelines reduce OpEx. Consultants who evaluate infrastructure with a cost lens routinely find waste that internal teams have normalized.
  • Risk mitigation and investment readiness. Avoiding compliance failures, security breaches, and data privacy violations lowers liability exposure. For firms preparing for fundraising or exit, strong engineering strategy raises valuation multiples by reducing perceived risk during due diligence integration, which aligns findings with the original investment premise to confirm viability.

Your Pre Search Checklist Before Talking to a Single Candidate

Auditing Technical Constraints Before You Begin Sourcing

Hiring the wrong consultant usually starts with a poorly defined problem. Before you contact a single candidate, audit three internal dimensions.

Architecture and Debt Audit

What problem must this hire solve first? Map your current system bottlenecks: performance degradation, scaling limitations, legacy frameworks, single points of failure. Quantify your technical debt across code coverage, documentation gaps, modularity, and observability. Identify emergent domain needs such as AI and ML readiness, data infrastructure maturity, or cloud migration requirements. Consultants use historical and scenario analysis to test the viability of investment theses; your architecture audit gives them the raw material to work with.

Team Dynamics and Autonomy Level

Will this consultant lead direct reports, act as a fractional CTO, or serve as an advisory voice? The answer changes the profile you need. An embedded specialist requires execution authority, including the ability to deprecate existing systems, enforce architectural standards, and own nonfunctional requirements. A dedicated pod model demands someone who can coordinate across multiple engineering teams. If your internal engineering leaders are not open to external guidance, even the best hire will stall. Strong management teams enhance investment success rates; this applies to the consultants who join them as well.

Deployment Model Dynamics

Remote versus in house is not a lifestyle preference; it is an operational decision with tradeoffs in collaboration speed, time zone overlap, and communication flow. A vetted dedicated remote talent model gives you access to a larger pool of senior engineers with domain experience across geographies. An in house FTE model gives you physical presence but adds recruitment friction that can stretch timelines to 60 or 90 days. Decide whether you need someone for a defined 90 day value inflection period, a prep for M&A, or a long term architecture leadership role.

Building a Profile That Attracts the Right Consultant, Not a Generic Job Spec

Four components separate an effective profile from a job board posting that attracts the wrong people:

  1. Core outcome and mission. State the objective in business terms. "Prepare our data platform for Series B diligence" is actionable. "Improve our technology" is not. Investment thesis testing evaluates core commercial logic of investments; your mission statement should reflect that specificity.
  2. Technical stack reality. Document your frameworks, languages, deployment environments, cloud providers, compliance requirements, and AI/ML maturity. Consultants who have experience in healthcare focused investment banking, for example, need to know your HIPAA posture before they can scope work.
  3. Decision making authority. Specify whether the consultant will be advisory or will have decision rights. Can they deprecate systems? Rewrite modules? Enforce standards? Who do they report to? Ambiguity here leads to organizational friction within weeks.
  4. Growth trajectory and longevity. Is this a contract to cover a spike, a preparation for exit, or a long term architecture leadership role? Investment strategy must align with an investor's risk tolerance, capacity, and preferences; the consultant's engagement model should mirror that alignment.
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How to Vet Technical Strategy Talent and Onboard for Immediate Impact

A Vetting Framework Built from Real Hiring Failures

Sourcing Reality

Traditional recruiters often take 60 to 90 days to fill senior technical strategy roles and charge large retainers. Prescreened engineering talent networks compress that timeline and deliver candidates with verified domain experience across regulated industries. Look for firms or consultants who have done similar work in your industry so they understand the compliance landscape, competitive dynamics, and technical constraints specific to your market. Consultants should have a track record of attracting venture capital or private equity investments, and that track record should be verifiable through references and case studies. Investment thesis consultants specialize in helping firms articulate and refine their investment rationales; you want someone who has done this before in your sector.

Technical Evaluation Pipeline

Stop testing for trivia. Senior investment thesis consultants operate at the system design and strategic execution level. Your evaluation pipeline should reflect that:

  • Live problem solving. Present a real architecture puzzle drawn from your system. Ask the candidate to design a high level solution under your current constraints, including budget, compliance, and timeline. Consultants can evaluate investments worth over $1 billion; test whether they can reason about your scale.
  • Real world scenario architecture review. Have the candidate analyze your existing codebase or architecture diagrams and identify tradeoffs across scaling, observability, and security. This reveals whether they think in systems or in features.
  • Communication under pressure. Ask them to explain a technical risk to a nontechnical stakeholder in real time. Independent research ensures objectivity in investment analysis, and you want to see that objectivity surface during a difficult conversation, not just in a polished slide deck.
  • Cross functional culture fit. Evaluate interactions with product, operations, and compliance leads. Consultants often have experience from top firms like McKinsey; what matters is whether their leadership style maps to your organization's decision cadence.

Consultants utilize a data driven methodology for market sizing and competitive benchmarking. Your vetting process should test for that rigor, not just pattern matching on resumes.

A 30/60/90 Day Protocol That Forces Immediate Ownership

Hiring is only half the problem. Onboarding determines whether you get ROI or a slow ramp that drains budget.

First 30 days: Diagnostic sprint. The consultant reviews system architecture, interviews key stakeholders, maps existing roadmaps and technical debt, and delivers a risk register. By day 30, you should have alignment on the top three strategic engineering risks and their connection to your investment thesis. Clients seek investment thesis testing before investment committee meetings; your consultant should be producing insights on that timeline, not still asking for access credentials.

Days 31 through 60: Strategy and plan formation. The consultant defines an architecture modernization or expansion roadmap, outlines prioritized tradeoffs, estimates cost and time for mitigations, and presents the execution plan. Quick wins surface here: performance improvements, cost savings, or compliance gap closures. Rigorous hypothesis stress testing involves independent data collection and expert interviews; your consultant should be conducting both during this phase.

Days 61 through 90: Measurable execution begins. At least one ROI vector should be demonstrated: reduced cloud cost, faster deployment cycles, a resolved critical failure, or a compliance gap closed. Communication channels, organizational buy in, and clear accountability for ongoing ownership should be confirmed. Consultants assess whether market growth supports investment valuations; by day 90, your consultant should have enough data to validate or challenge your thesis assumptions.

Evaluation Signals, Strategic Advantage, and Next Steps

What to Watch for During the Interview Process

Red flags that predict failure:

  • Tool obsession over system thinking. If a candidate leads with specific technologies instead of architectural vision, they are selling their resume, not solving your problem. Consultants evaluate market growth and pricing assumptions; a tool focused candidate will miss the business context entirely.
  • Inability to discuss past failures. Every senior consultant has made wrong calls. If they cannot articulate a decision that went sideways and what they learned, they either lack the experience they claim or lack the transparency you need.
  • Generic pattern matching without specifics. Quoting architectural patterns without giving examples from real systems suggests theoretical knowledge without execution depth. Testing includes market sizing and competitive assessments; your consultant should be able to reference specific engagements where they performed that work.
  • Low tolerance for ambiguity. Investment thesis work involves messy, incomplete data. If a candidate requires rigid specifications before they can proceed, they will stall in the real world conditions of pre deal diligence. Consultants can help assess revenue durability and growth scalability, but only if they can operate with imperfect information.

Green flags that predict success:

  • Pragmatic tradeoff analysis. The candidate compares multiple possible paths and selects based on total cost plus impact, not personal preference. Portfolio synergies identification evaluates how new assets complement existing holdings; a strong consultant thinks in portfolios, not projects.
  • Focus on data quality and system integrity. They ask about observability, data pipelines, and error rates before they ask about features. Consultants analyze customer retention and revenue drivers for investments; that analysis requires clean data and reliable systems.
  • Proactive risk identification. They anticipate scaling or compliance issues before you raise them. Investment thesis consultants can validate growth narratives; the best ones challenge those narratives early.
  • Cross functional influence. They have driven consensus across product, operations, security, compliance, and finance teams. This is the skill that separates advisors from leaders. Consultants validate pricing and retention assumptions during testing; that validation requires collaboration across the organization.

Why SoftDoes Is the Partner for This Hire

SoftDoes is a North America focused custom software engineering, data, and AI partner serving clients across the US and Canada. When you hire an investment thesis consultant through our architecture consulting practice, you get:

  • Battle tested senior talent. Every consultant in our network has led technical diligence, architecture modernization, or platform scaling for enterprise clients in regulated industries. Investment managers in our network have raised or intermediated over €1 billion in capital deployments; they bring that experience to your engagement.
  • Engineering led delivery oversight. This is not an unmanaged freelancer marketplace. SoftDoes provides CTO level guidance and structured delivery, ensuring your consultant operates with the support and accountability of a full engineering organization. It assesses whether market growth is as attractive as presented, with the rigor that PE firms and investment committees expect.
  • Rapid deployment capability. When a deal timeline compresses, you need someone who can begin within days, not months. Our prescreened network means you can select and deploy consultants on the timeline your investment demands.
  • Flexibility to scale up or down. Engagement models range from dedicated hire to fractional retainer to project based sprints. Scale your team as the investment thesis evolves, without the friction of traditional recruitment.
  • Zero risk replacement guarantee. If the consultant is not the right fit, we provide an immediate replacement. Clients can work with consultants for a no risk trial period; your budget and timeline are protected.

Asset allocation is a primary strategic decision in portfolio construction. The same principle applies to allocating your engineering budget: place senior talent where it generates the greatest return per dollar of spend. Investment thesis testing helps determine if forecasts are achievable; SoftDoes ensures you have the technical leadership to execute against those forecasts.

Your Next Step

If you are preparing for M&A diligence, fundraising, platform scaling, or entry into a regulated market, the cost of a slow or misaligned hire compounds with every week of delay. Book a technical discovery session with SoftDoes architects to scope your engagement, define the ideal consultant profile, and begin sourcing from our senior talent network. Contact us to connect with a principal who has led engagements in your industry and can help you achieve results on the timeline your investment thesis demands.

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