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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 Keyword Researchers 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 Keyword Researcher

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 a Keyword Researcher through SoftDoes?

Fully in house hiring cycles for senior keyword research roles often take 8 to 12 weeks from job posting to onboarding. Through SoftDoes, that timeline compresses to roughly 4 to 6 weeks including multi stage vetting, technical challenge evaluation, and offer finalization. The acceleration comes from maintaining a pre vetted network of senior talent with proven enterprise SEO and keyword research expertise, eliminating the months typically spent screening unqualified candidates.

What does it cost to hire a Keyword Researcher?

Salaries in the US for senior keyword research specialists vary by market, scope, and industry experience. Average base pay sits around $76,000 per year, with the middle 50 percent falling between roughly $59,000 and $99,000. Junior or mid level researchers may range from $45,000 to $85,000 depending on location and whether the role is remote or local. For senior or enterprise scope positions that span global content, technical SEO ownership, and multi locale keyword strategies, total compensation can exceed $100,000. SoftDoes engagement pricing reflects the seniority and capability of the talent deployed, with transparent cost structures that eliminate the hidden overhead of traditional recruitment.

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

Three primary models exist: full time in house FTE, remote dedicated hire with consistent oversight, and agency or contractor models for short term or project based work. Each carries different tradeoffs in cost, ownership, and integration depth. SoftDoes offers dedicated senior hires who embed with your team, pod structures that combine keyword research with broader SEO and content marketing disciplines, and flexible contract arrangements. All models include engineering led delivery oversight, the ability to scale up or down as your business needs evolve, and the zero risk replacement guarantee to offset the cost of a mismatch.

How do you ensure time zone alignment with a Keyword Researcher?

Effective collaboration requires clear overlap hours, typically 2 to 4 hours daily, for synchronous work with engineering, product, and content teams at headquarters. SoftDoes prioritizes candidates within acceptable time zones and establishes structured communication protocols: daily standups, documented SOPs, and well defined asynchronous workflows. For roles that require close coordination on search engine optimization deployments or real time competitor analysis, SoftDoes matches talent with proven remote discipline and experience operating across distributed teams.

How does SoftDoes technically vet a Keyword Researcher?

SoftDoes runs a multi stage evaluation pipeline. Candidates complete a technical challenge based on a real client business case, covering topic cluster design, search intent mapping, and priority versus difficulty analysis. They undergo an architecture review scenario that tests their ability to diagnose and remediate real world problems like crawl issues, content overlap, and indexation bloat. Interviews evaluate communication clarity with non technical stakeholders, cross functional collaboration instincts, and tool fluency across platforms like Google Search Console, Ahrefs Semrush, and enterprise analytics suites. Past work portfolios are reviewed for evidence of traffic forecasting, intent mapping, and measurable business outcomes. This process ensures every keyword researcher deployed is a senior practitioner, not an order taker.

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

SoftDoes provides a zero risk replacement guarantee: if you identify a mismatch within the agreed period, a replacement is deployed without additional cost or delay. Engagement structures are designed for flexibility. Scaling up means adding more team members or expanding scope across additional product lines, locales, or content verticals. Scaling down means refocusing scope or reducing team size while maintaining core outputs, governed by clear SLAs and milestone agreements. This flexibility ensures you never carry unnecessary fixed headcount while still maintaining continuity in your search engine optimization and content strategy execution.

The Executive Guide to Hiring a Keyword Researcher

A misaligned keyword researcher does not just waste a salary line; it quietly bleeds six figures in lost organic traffic, misdirected content marketing spend, and engineering rework that compounds every quarter. A strong one accelerates your entire search engine optimization pipeline and turns keyword research into a revenue lever. This playbook gives you a field tested strategy to define, vet, and onboard top tier keyword research talent, built from lessons learned across dozens of enterprise deployments.

What Actually Separates a Senior Keyword Research Expert from an Order Taker

The True Scope: Ownership, Systems, and Tradeoff Management

Most job descriptions for keyword researchers read like a tools checklist: knows Ahrefs, Semrush, Google Keyword Planner. That tells you nothing about whether this person can own outcomes. Here is what senior keyword research experts actually do every day in enterprise environments:

  • Govern the topic and hierarchy architecture. They design scalable keyword clusters across product lines, brands, and locales. This means anticipating content overlap, managing international SEO taxonomies, and building systems that do not collapse when you launch a new vertical or landing pages for a new market segment.
  • Translate search intent into roadmap priorities. A senior researcher maps informational, commercial, transactional, and navigational queries to real user journeys, then produces content briefs that align with revenue pipelines, not just keyword rankings.
  • Manage volume vs. difficulty vs. CPC tradeoffs. Effective keyword research means knowing when to pursue long tail keywords with high conversion intent versus chasing vanity search terms with inflated volume but low return. They balance keyword difficulty against resource investment and time to rank.
  • Build repeatable systems, not one off spreadsheets. This includes dashboards with BI grade reporting, automated clustering workflows, defined taxonomies, and processes that survive a tool migration. They use Google Search Console for first party query data, integrate crawl logs, and pull from internal search data to surface keyword opportunities invisible to external SEO tools.
  • Drive cross functional alignment. The best keyword researchers work regularly with engineering on site structure, crawl budget, and speed optimization; with product teams on feature taxonomy; with legal on regulated content; and with the content team on editorial calendars. They anticipate dependencies and blockers before they slow your deployment cycles.
  • Own risk and technical debt in search performance. Content decay, keyword cannibalization, orphaned pages, core web vitals degradation, and indexation bloat are all in their domain. Keyword to URL mapping prevents keyword cannibalization on sites. They serve as a guardrail for on page SEO compliance and search visibility integrity.

Keyword research is commonly part of a broader SEO role, but at enterprise scale the discipline demands dedicated ownership. In fact, 90.4% of SEO specialist positions include keyword research as a core responsibility, reinforcing that this is foundational for effective SEO strategies.

The Business Case: Financial and Operational Impact

The cost of a bad hire here is not theoretical. It is measured in wasted content production, missed conversion windows, and engineering hours burned on rework. Four concrete ROI vectors justify getting this right:

  • Reduced opportunity cost. If keyword research misidentifies intent or misses high value clusters, marketing spends on content that drives qualified traffic nowhere near your conversion funnel. Choosing the wrong keywords can waste months of time and resources while competitors capture the organic visibility you should own.
  • Technical debt reduction. Mismanaged page seo, crawl issues, canonical problems, and indexation bloat slow down deployments and break features. A senior researcher spots these and works cross functionally with engineering to resolve them, preventing compounding rework.
  • Infrastructure and tooling optimization. The right hire standardizes your tool stack (Google Search Console, GA4, Ahrefs Semrush, enterprise crawlers), automates reporting, and eliminates redundant manual work. Hiring a keyword researcher can save over 18 hours of effort per research cycle by replacing fragmented manual processes with systematic workflows.
  • Acceleration of go to market cycles. With the right SEO expert embedded, you get faster decision making, better content prioritization, fewer rounds of content removal and creation, and tighter alignment with product or campaign launches. One case study showed a rigorous, intent driven keyword methodology delivered a 317% increase in organic traffic and a 584% uplift in marketing sourced qualified leads over an 18 month window.

Before You Post a Job Description: The Pre Search Audit

Audit Your Technical Constraints Before Searching for Candidates

Hiring without understanding what problem the hire must solve first is how you end up with a keyword research specialist who cannot actually move the needle. Audit three dimensions before you write a single job requirement.

Architecture and Debt Audit

What structural issues are blocking growth right now? Assess indexation bloat, content taxonomy conflicts mixing intents or overlapping product pages, missing canonicalization, poor internal linking, and template inconsistencies. Evaluate your site's technical SEO health: page speed, mobile friendliness, core web vitals (LCP, CLS), schema markup, URL structure, crawlability, and duplicate or thin content. If your CMS is restrictive or past decisions limit what a researcher can actually change, document those constraints. Existing content debt, including decayed pages, outdated topics, and redesigns that ignored search engine optimization, shapes the profile you need.

Team Dynamics and Autonomy Level

Will this researcher operate within marketing, under content, product, or engineering? An embedded specialist may have less leverage over architecture, while a dedicated pod can own more. Clarify the level of collaboration expected with engineering, product, content, localization, and legal. Determine how mature your cross team processes are and how decision rights are allocated. Critically assess your existing SEO maturity: are there processes, tools, and measurement already in place, or is this hire creating everything from scratch?

Deployment Model Dynamics

In house full time employees offer deeper integration, domain knowledge, and control, but come with higher fixed costs and longer lead times. Dedicated remote hires unlock global talent pools at lower cost, but proven remote discipline and communication clarity are rare. Contractors or agencies provide fast ramp and limited risk, but often deliver lower ownership, weaker knowledge transfer, and minimal continuity. Modern talent deployment models that pair dedicated senior talent with engineering led oversight can bridge these tradeoffs, combining the ownership depth of an FTE with the flexibility of contract engagement.

Engineering the Ideal Profile, Not a Generic Job Spec

Stop writing job descriptions that say "5+ years SEO experience" and start aligning four critical profile dimensions to your actual business needs:

  • Core Outcome and Mission. Define the mission in context. Is this hire redesigning taxonomy, driving international growth, fixing technical debt, supporting content scale, or enabling product acquisitions? Anchor the role to measurable outcomes: share of voice, non brand organic traffic, assisted revenue contribution, content ROI, or conversion rate lifts.
  • Technical Stack Reality. Document which SEO tools are in use. Do you leverage automation or AI for clustering, topic modeling, or intent classification? Identify all data sources: internal search logs, customer support queries, product usage data, competitor analysis tools, paid campaigns input, crawl logs, and performance metrics. Expect technical literacy: ability to work with developers on schema, internal linking, sitemaps, JavaScript SEO, rendering, and page optimization.
  • Decision Making Authority. Will this person own prioritization for the content strategy roadmap? Will they have decision rights over topic prioritization, budget for tools, or authority to request changes from engineering? Define clear escalation paths and speed of decision flow.
  • Growth Trajectory. What is the career path? Transparency about whether this person can grow into a lead SEO strategist or head of organic growth determines the caliber of candidate you attract. Define how performance will be judged beyond KPIs and what development investment you will provide.
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The Vetting and Onboarding Playbook

A Battle Tested Framework for Evaluating Keyword Research Talent

Sourcing Reality

Traditional recruiters surface resumes. Prescreened talent networks surface practitioners who have already proven they can do the work. The difference matters when you are hiring for a role where the wrong candidate can quietly misdirect your entire content marketing engine for two quarters before anyone notices. SEO and marketing communities can provide networking opportunities for hiring keyword researchers, and freelance platforms can be effective for finding skilled SEO professionals who work as a freelance keyword researcher. Clients rate Toptal keyword researchers 4.9 out of 5.0, and average time to match a keyword researcher is under 24 hours, but speed without vetting depth is its own risk. Look for candidates who have worked on regulated industries, global sites, or multi locale SEO, and who have led enterprise scale keyword research rather than small site blogs.

Technical Evaluation Pipeline

Ditch trivia questions. Practical skill assessments like a test project can help evaluate candidates far better than asking them to define "search volume." Use these evaluation layers:

  • Live problem solving. Give them a real business case: "Choose 3 topic clusters for our product area, map out search intent, priority vs. difficulty, and expected traffic and revenue impact." This tests whether they can identify relevant keywords and build a prioritized SEO opportunity map, or just generate keyword lists.
  • Architecture review scenario. Present part of your site's actual problem: crawl log anomalies, index bloat, content overlap, or poor interlinking. Ask their remediation plan. Candidates should evaluate keyword difficulty by assessing content quality and backlink profiles, not just relying on a single tool metric.
  • Communication under pressure. Test their ability to explain complex technical SEO issues to non SEO stakeholders in product, engineering, or legal. A keyword research expert who cannot communicate tradeoffs to a VP of Product is a bottleneck, not a force multiplier.
  • Cross functional culture fit. Scenario: engineering says the topic hub requires framework changes, content wants a quick publish, product demands a feature launch takes priority. How does the candidate balance these? This reveals whether they can operate as a true digital marketing expert or only function in a silo.
  • Past failures and learning. Ask for times when a keyword strategy went wrong: keyword rankings dropped, intent was misunderstood, a project was misprioritized. Their self analysis reveals maturity. It is advisable to require proof of past results when hiring keyword researchers. Portfolio review should include examples of search intent mapping and traffic forecasting.

Good candidates will validate keyword intent by examining the actual search engine results pages, not just trusting tool outputs. Employers are expanding expectations beyond traditional rankings toward broader search visibility and AI driven discovery, including answer engine optimization and generative engine optimization strategies.

A Frictionless Ramp Up Protocol for the First 90 Days

A structured 30/60/90 day milestone roadmap transforms a new hire from observer to owner with measurable results:

Days 1 through 30: Discovery and Baseline. Full access to all tools and data: Google Search Console, analytics, crawl reports, CMS, internal search logs. Stakeholder interviews with engineering, product, content, and legal. Baseline all metrics: current content performance, organic traffic by topic, index coverage, technical SEO health. Identify a few "small wins" (fix a high impact technical issue, optimize a near ranking page) to build credibility and demonstrate effective keyword research capability.

Days 31 through 60: Action and Content Mapping. Apply keyword research insights: produce prioritized topic clusters; map content gaps against competitor keyword gap analyses that show search terms competitors rank for; start brief creation. Work with engineering to fix blocking issues around site structure, page speed, or render problems. Establish a recurring measurement and reporting rhythm. Deliverables should include long tail and question based keyword sets alongside the master keyword list segmented by topic cluster. Tools like AnswerThePublic surface question based queries that feed content creation and local SEO strategies. Keyword research tools such as SEMrush, Ahrefs, and Moz help identify both long tail keywords and high value target keywords. Keyword research typically includes search volume and keyword difficulty data, plus CPC data for terms that overlap with paid campaigns.

Days 61 through 90: Execution and Momentum. Execute deeper campaigns or experiments. Push content, topic hubs, and international or local content work. Show early signals: indexing improvements, non brand impressions growth, click through rate improvements, conversion lifts. Evaluate whether the developer backlog is permitting progress, content production cadence is stable, tools are working, and team dependencies are resolved. A well structured keyword list now guides content and SEO campaigns moving forward. Effective keyword research at this stage should significantly improve organic visibility and drive organic traffic that compounds quarter over quarter.

Making the Final Decision

Interview Signals That Reveal Red Flags and Green Flags

Red Flags:

  • Volume obsession without outcome thinking. If a candidate can name ten SEO tools and cite search trends but cannot map those to revenue or business goals, they will produce keyword lists that go nowhere. Search volume alone is a poor hiring criterion. Keyword researchers should not chase every high volume keyword.
  • Inability to discuss past failures. If every case study is a success story and they cannot articulate a time their strategy misfired, they either lack professional experience or lack self awareness. Both are disqualifying.
  • Tool obsession over problem solving. Advocating for buying new tools rather than establishing process discipline or optimizing existing page seo is a sign of a junior mindset. A real SEO specialist solves problems with what is available first.
  • No experience with technical constraints. If they cannot discuss crawl budget, speed optimization, architecture dependencies, localization, or regulatory constraints, they will be unable to operate in your engineering environment. Keyword research should involve understanding user intent and competition levels, not just generating data exports from Google Sheets.

Green Flags:

  • Pragmatic tradeoff analysis. The candidate shows cases where they chose "good enough" over "perfect" to accelerate measurable results. They understand that keyword strategies must adapt to resource constraints and competitive realities.
  • Emphasis on data quality and system integrity. Not only keyword metrics like volume and difficulty, but conversion quality, user intent analysis, click through rates, SERP features, and best keywords for the actual target audience.
  • Proactive risk identification. They surface risks like content cannibalization, over optimizing generic content, duplicate content, and indexation issues before you ask. They think about competitor analysis as a continuous discipline, not a one time report.
  • Cross functional orientation. Examples of working with engineering, product, compliance, or localization teams. Evidence of influencing roadmaps, persuading stakeholders, and shipping work that required tailored strategies across departments. This is what separates a digital marketing expert from someone who just runs keyword tools.

Why SoftDoes Operates as a Strategic Advantage

SoftDoes delivers what traditional recruitment cannot: senior, battle tested talent with engineering led oversight, zero risk replacement guarantee, flexible scale up and scale down capability, and measurable ROI from day one. Unlike unmanaged freelancer platforms, every keyword researcher deployed through SoftDoes operates within a structured delivery framework. Unlike agencies that deliver volume or junior analysts, SoftDoes provides enterprise grade strategists with system ownership and direct accountability for business outcomes. When you need to hire market intelligence analysts or keyword experts who can also drive broader competitive intelligence, the same vetting rigor applies.

The Path Forward: Your Next Move

Every week without the right keyword researcher in your pipeline is a week your competitors compound their organic visibility advantage while your content strategy stalls. The playbook above gives you the framework. If you want to skip the 8 to 12 week traditional hiring cycle and deploy a vetted, senior keyword research specialist within weeks, book a technical discovery session with SoftDoes architects. No long term commitment. No hiring gamble. Just the right expertise, deployed fast.

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