Remote AI Platform Engineer Jobs
Remote AI Platform Engineer jobs are in active demand across the U.S., with remote-first firms and distributed engineering teams hiring for roles in cloud infrastructure, MLOps, and enterprise AI across software, finance, and healthcare. Employers hiring remotely right now include CVS Health, phData, and BV Teck. Scan the live roles below and apply to whichever ones fit.
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Showing 4 of 150+ Remote AI Platform Engineer jobs
Senior AI QA Platform Engineer – Agentic QA Orchestration (Amazon Bedrock)
Company: BONbLOC Technologies
Location: Remote (Candidates must reside in or around Chicago Area, Midwest, New Jersey, or New York)
Role Type: Full-Time / Contract
Seniority Level: Senior Level (7+ Years Experience)
Position Summary
We are seeking a Senior AI QA Platform Engineer to design and implement an enterprise AI-enabled QA Orchestration Platform leveraging Amazon Bedrock, AgentCore, Playwright, AWS CodeBuild, GitHub, Databricks, Jira, and qTest.
This platform will utilize agentic AI workflows to generate and maintain automated tests, orchestrate execution paths, classify test failures, create actionable defects, and provide governance, observability, traceability, and auditability across the QA lifecycle. The ideal candidate combines expertise in software engineering, cloud-native platforms, AI/LLM integration, CI/CD automation, and quality engineering.
Primary Responsibilities
- Agentic AI Orchestration: Design and implement AI-powered QA orchestration workflows using Amazon Bedrock, AgentCore, and Multi-Agent Architectures.
- Autonomous QA Capabilities: Develop agent-based automation capabilities for test generation, self-healing test maintenance, failure classification, defect routing, and test intelligence/analytics.
- Framework & Repository Management: Integrate AI workflows with Playwright frameworks and build automated GitHub branch creation, pull request generation, and repository-aware test generation patterns.
- CI/CD & Quality Gates: Design and implement integrations with AWS CodeBuild and enterprise CI/CD pipelines, incorporating rule-based quality gates and AI-assisted failure analysis.
- Enterprise Platform Integrations: Build integrations with Databricks, Jira, qTest, and GitHub, leveraging Databricks Test Data Management (TDM) and analytics for historical context and failure classification.
- AI Governance & Observability: Design and implement AI governance controls including prompt version control, Model Context Protocol (MCP), approved action policies, audit logging, human-in-the-loop workflows, and AI action traceability.
- Cross-Functional Collaboration: Partner with QA, DevOps, Cloud Engineering, Data Engineering, and Security teams to establish enterprise AI testing standards and platform architecture.
Required Technical Skills
- AI & Agentic Systems: Claude, Prompt Engineering, Agentic AI Workflows, Multi-Agent Architectures, Model Context Protocol (MCP).
- Preferred Agent Frameworks: Strands Agents, Amazon Bedrock, AgentCore.
- Programming & Scripting: TypeScript, Node.js, JavaScript, SQL, REST API Development.
- Cloud & AWS: AWS Services (IAM, CodeBuild, CloudWatch, S3, Secrets Manager, Parameter Store).
- DevOps & CI/CD: GitHub, GitHub Actions, CI/CD Pipelines, Infrastructure as Code (Terraform preferred).
- Automation & Enterprise Tools: Playwright, API Testing, Test Automation Framework Architecture, Test Data Management Concepts, Jira, qTest, Databricks.
Preferred Qualifications
- Experience: 7+ years of software engineering, automation engineering, platform engineering, or cloud engineering experience, with 5+ years designing and implementing enterprise test automation solutions.
- AI & Cloud Mastery: Experience building cloud-native solutions on AWS and integrating AI/LLM capabilities into engineering or developer automation platforms.
- Governance & Analytics: Track record of designing governance, auditability, compliance, and operational controls, as well as working with Databricks, TDM, or analytics-driven automation solutions.
- Location Requirements: Candidates must reside in or around the Chicago Area, Midwest, New Jersey, or New York regions.
- Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
Preferred Certifications
- AWS Certified Solutions Architect
- AWS Certified AI Practitioner
- AWS Certified Developer – Associate
- HashiCorp Certified: Terraform Associate
Pay: $95,000.00 - $100,000.00 per year
Work Location: Remote
Senior AI QA Platform Engineer – Agentic QA Orchestration (Amazon Bedrock)
Company: BONbLOC Technologies
Location: Remote (Candidates must reside in or around Chicago Area, Midwest, New Jersey, or New York)
Role Type: Full-Time / Contract
Seniority Level: Senior Level (7+ Years Experience)
Position Summary
We are seeking a Senior AI QA Platform Engineer to design and implement an enterprise AI-enabled QA Orchestration Platform leveraging Amazon Bedrock, AgentCore, Playwright, AWS CodeBuild, GitHub, Databricks, Jira, and qTest.
This platform will utilize agentic AI workflows to generate and maintain automated tests, orchestrate execution paths, classify test failures, create actionable defects, and provide governance, observability, traceability, and auditability across the QA lifecycle. The ideal candidate combines expertise in software engineering, cloud-native platforms, AI/LLM integration, CI/CD automation, and quality engineering.
Primary Responsibilities
- Agentic AI Orchestration: Design and implement AI-powered QA orchestration workflows using Amazon Bedrock, AgentCore, and Multi-Agent Architectures.
- Autonomous QA Capabilities: Develop agent-based automation capabilities for test generation, self-healing test maintenance, failure classification, defect routing, and test intelligence/analytics.
- Framework & Repository Management: Integrate AI workflows with Playwright frameworks and build automated GitHub branch creation, pull request generation, and repository-aware test generation patterns.
- CI/CD & Quality Gates: Design and implement integrations with AWS CodeBuild and enterprise CI/CD pipelines, incorporating rule-based quality gates and AI-assisted failure analysis.
- Enterprise Platform Integrations: Build integrations with Databricks, Jira, qTest, and GitHub, leveraging Databricks Test Data Management (TDM) and analytics for historical context and failure classification.
- AI Governance & Observability: Design and implement AI governance controls including prompt version control, Model Context Protocol (MCP), approved action policies, audit logging, human-in-the-loop workflows, and AI action traceability.
- Cross-Functional Collaboration: Partner with QA, DevOps, Cloud Engineering, Data Engineering, and Security teams to establish enterprise AI testing standards and platform architecture.
Required Technical Skills
- AI & Agentic Systems: Claude, Prompt Engineering, Agentic AI Workflows, Multi-Agent Architectures, Model Context Protocol (MCP).
- Preferred Agent Frameworks: Strands Agents, Amazon Bedrock, AgentCore.
- Programming & Scripting: TypeScript, Node.js, JavaScript, SQL, REST API Development.
- Cloud & AWS: AWS Services (IAM, CodeBuild, CloudWatch, S3, Secrets Manager, Parameter Store).
- DevOps & CI/CD: GitHub, GitHub Actions, CI/CD Pipelines, Infrastructure as Code (Terraform preferred).
- Automation & Enterprise Tools: Playwright, API Testing, Test Automation Framework Architecture, Test Data Management Concepts, Jira, qTest, Databricks.
Preferred Qualifications
- Experience: 7+ years of software engineering, automation engineering, platform engineering, or cloud engineering experience, with 5+ years designing and implementing enterprise test automation solutions.
- AI & Cloud Mastery: Experience building cloud-native solutions on AWS and integrating AI/LLM capabilities into engineering or developer automation platforms.
- Governance & Analytics: Track record of designing governance, auditability, compliance, and operational controls, as well as working with Databricks, TDM, or analytics-driven automation solutions.
- Location Requirements: Candidates must reside in or around the Chicago Area, Midwest, New Jersey, or New York regions.
- Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience.
Preferred Certifications
- AWS Certified Solutions Architect
- AWS Certified AI Practitioner
- AWS Certified Developer – Associate
- HashiCorp Certified: Terraform Associate
Pay: $95,000.00 - $100,000.00 per year
Work Location: Remote
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Find JobsRemote AI Platform Engineer Job Market
Who's Hiring
- CVS Health53
- phData2
- BV Teck2
- Netflix2
- Blackbaud2
Top Industries Hiring
- Healthcare & Medical Services51
- Technology & Software11
- Consulting & Professional Services4
- Education2
- Banking & Financial Services2
What Employers Look For
The qualifications that appear most often in remote AI platform engineer jobs.
- Proficiency in Python and at least one infrastructure-as-code tool such as Terraform
- Hands-on experience with Kubernetes, Docker, and container orchestration at scale
- Experience building or maintaining MLOps pipelines using tools like Kubeflow, MLflow, or similar
- Familiarity with major cloud platforms including AWS, Google Cloud, or Microsoft Azure
- Bachelor's degree in computer science, engineering, or a related technical field
- Experience with data pipeline frameworks such as Apache Spark, Airflow, or Ray
Tips for Your Remote AI Platform Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote ai platform engineer openings from across the U.S. in one place. Search for roles that match your stack and experience level and apply directly, since remote postings at well-known companies fill quickly.
Document your async collaboration experience
Remote employers hire ai platform engineers who can move projects forward without constant check-ins. Your resume and cover letter should name specific async tools you've used, like Notion, Linear, or Slack threads, and show decisions you made and shipped independently.
Build a public AI infrastructure portfolio
Put your MLOps pipelines, model serving setups, or LLM orchestration work somewhere reviewable, whether a GitHub repository, a technical blog, or a demo environment. Remote hiring managers evaluate your work before they ever schedule a call.
Prepare for remote-first technical interviews
Expect live coding sessions in shared environments and system design discussions over video. Practice explaining your architecture decisions out loud and in writing, since remote teams assess both your technical depth and your ability to communicate complex infrastructure choices clearly.
Remote AI Platform Engineer Jobs: Frequently Asked Questions
How do I get a remote ai platform engineer job?
Target remote-first companies and distributed engineering teams, since they have established workflows for async collaboration and are more likely to onboard remote engineers successfully. Remote employers screen heavily for self-direction, clear written communication, and hands-on experience with MLOps tooling, cloud platforms, and LLM orchestration frameworks. Candidates who show previous async project delivery or open-source contributions in AI infrastructure stand out.
Which companies hire remote ai platform engineers?
Companies hiring remote ai platform engineers right now include CVS Health, phData, and BV Teck, based on current remote listings on Migrate Mate as of August 2026. Remote openings for this role concentrate at remote-first software companies, AI-native startups, and distributed teams in fintech and cloud services, where engineering teams operate across time zones and rely on async workflows.
Can you get a remote ai platform engineer job with no experience?
Yes, but remote entry-level roles are harder to land because employers expect you to work independently from day one without in-person onboarding support. The strongest path is building a public portfolio of AI infrastructure projects, contributing to open-source MLOps or LLM tooling repositories, and demonstrating written communication skills through documentation or technical writeups that show you can collaborate async.
Do you need a degree for remote ai platform engineer jobs?
Not always. Remote employers weigh demonstrated skills, shipped projects, and hands-on experience with cloud platforms, model serving infrastructure, and MLOps pipelines more heavily than a specific credential. A portfolio showing real AI infrastructure work, certifications from major cloud providers, and contributions to relevant open-source projects can carry as much weight as a formal degree for many remote teams.
Which industries hire the most remote ai platform engineers?
Most remote ai platform engineer openings sit in Healthcare & Medical Services, Technology & Software, and Consulting & Professional Services, per current remote listings on Migrate Mate as of August 2026. These sectors rely on distributed engineering teams that build and maintain AI infrastructure across multiple regions, making remote work a natural fit for the role.
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