Remote AI Agent Engineer Jobs
Remote AI Agent Engineer jobs are open across software development, fintech, and enterprise AI platforms at remote-first companies and distributed teams building production-grade agent systems. Employers hiring remotely right now include ServiceNow, University of San Diego, and QUO, with openings from early-career roles to senior engineers leading agent architecture. Scan the live roles below and apply to whichever ones fit.
Find Remote AI Agent Engineer JobsOverview
Showing 5 of 47+ Remote AI Agent Engineer jobs
Job ID:
J54932
Job Title:
Senior AI Agent Engineer
Location:
Charlotte, NC
Duration:
24 Months + Extension
Domain Exposure:
Consumer Staples
Client:
To Be Discussed Later
Employment Type:
W-2, 1099, C2C
Work Authorization:
US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other
Job Title: Senior AI Agent Engineer
Location: Charlotte, NC(Remote)
Duration: 24 Months + Extension
Bill Rate: $69/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other
Job Description:
Design, develop, and harden production-grade AI agents across the end-to-end detect store plan remediate deploy lifecycle.
Build and orchestrate Finder, Planner, and Executor agents to identify issues, prioritize remediation, execute approved actions, validate changes, and support controlled deployments.
Develop agent orchestration and tool integrations using Model Context Protocol (MCP), tool calling, agent frameworks, and workflow engines such as Temporal.
Develop and integrate agents with the Actionable Observation store, Overwatch state machine and prioritization capabilities, and operator console.
Integrate agents with GitLab pipelines, GitLab CI/CD, JFrog Artifactory, canary deployment workflows, and Tenable Vulnerability Management (UVM) as a vulnerability-finding source.
Expose and consume enterprise knowledge graphs containing data lineage and environment context through MCP, ensuring agents have governed, read-only access to relevant information.
Implement event processing, data storage, and state management using Kafka, MongoDB, SQL, and SQLite.
Develop and query knowledge graphs using Neo4j and Cypher to support contextual retrieval, dependency analysis, and remediation planning.
Implement automated code-change workflows, AI-assisted code generation, test generation, and lockdown-test practices to validate remediation changes before deployment.
Apply context engineering and specification-driven development to improve agent accuracy, consistency, traceability, and maintainability.
Embed human-approval gates, audit trails, access controls, and AI governance controls throughout agent design, execution, and deployment.
Ensure autonomous workflows comply with enterprise security standards, change-management processes, deployment controls, and production-readiness requirements.
Support vulnerability remediation and software version-currency initiatives using CVE, EPSS, CVSS, End-of-Life (EOL), and End-of-Support (EOS) considerations.
Implement controlled deployment patterns, including canary releases and incremental rollouts, to reduce operational risk.
Troubleshoot agent execution failures, integration issues, workflow exceptions, and deployment problems; perform root-cause analysis and implement corrective actions.
Monitor and improve agent reliability, performance, scalability, and observability using Dynatrace or comparable tools where applicable.
Collaborate with application development, infrastructure, cybersecurity, DevSecOps, architecture, and production support teams to integrate agents into existing workflows.
Document reusable agent patterns, technical designs, integration approaches, governance controls, onboarding procedures, and operational best practices.
Coach application, infrastructure, and security teams on agent onboarding, secure usage, human oversight, and adoption of the autonomous operating model.
Participate in technical design reviews, code reviews, automated testing, release planning, and continuous improvement initiatives.
Own AI agent solutions across requirements, design, development, integration, testing, deployment, monitoring, and ongoing enhancement.
Basic Qualifications:
Minimum 7+ years of experience in software engineering or production application development, delivering and supporting enterprise-grade solutions.
Minimum 1–2+ years of hands-on experience developing LLM-based applications, Generative AI solutions, AI agents, or agentic workflows.
Minimum 3+ years of hands-on experience developing production software using Python and/or Java.
Minimum 3+ years of experience with Git, GitLab CI/CD, automated testing, and software delivery practices.
Minimum 1+ year of hands-on experience with AI agent orchestration, MCP, tool calling, and agent frameworks or equivalent technologies.
Minimum 1+ year of experience with agent development platforms such as AWS Agent Core, Kiro, or equivalent technologies.
Minimum 1+ year of experience with workflow orchestration engines such as Temporal or equivalent platforms.
Minimum 2+ years of experience developing automated code-change workflows, code validation, automated test generation, or lockdown-test practices.
Minimum 1+ year of practical experience applying context engineering and specification-driven development to AI or software engineering workflows.
Minimum 2+ years of experience working with event-processing or data-storage technologies such as Kafka, MongoDB, SQL, or SQLite.
Minimum 1+ year of experience with Neo4j, Cypher, graph databases, or knowledge-graph querying.
Minimum 3+ years of experience integrating applications with CI/CD pipelines, artifact repositories, deployment platforms, or enterprise tools.
Minimum 2+ years of experience implementing secure software engineering practices, access controls, audit trails, or human-in-the-loop approval mechanisms.
Minimum 1+ year of experience implementing AI governance, security guardrails, or operational controls for AI-driven automation.
Minimum 3+ years of experience with SDLC, automated testing, change management, release processes, and production support.
Minimum 3+ years of experience troubleshooting complex application or integration issues, conducting root-cause analysis, and implementing corrective actions.
Minimum 2+ years of experience collaborating with cross-functional teams, including application development, infrastructure, DevOps, or cybersecurity.
Minimum 1+ year of experience documenting technical designs, reusable engineering patterns, operational procedures, or implementation standards.
Minimum 1+ year of experience providing technical guidance, mentoring engineers, or coaching teams on engineering practices or new technology adoption.
Demonstrated ability to explain personal technical contributions, agent architecture, implementation decisions, challenges, and business outcomes from previous projects
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.
Job ID:
J54932
Job Title:
Senior AI Agent Engineer
Location:
Charlotte, NC
Duration:
24 Months + Extension
Domain Exposure:
Consumer Staples
Client:
To Be Discussed Later
Employment Type:
W-2, 1099, C2C
Work Authorization:
US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other
Job Title: Senior AI Agent Engineer
Location: Charlotte, NC(Remote)
Duration: 24 Months + Extension
Bill Rate: $69/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other
Job Description:
Design, develop, and harden production-grade AI agents across the end-to-end detect store plan remediate deploy lifecycle.
Build and orchestrate Finder, Planner, and Executor agents to identify issues, prioritize remediation, execute approved actions, validate changes, and support controlled deployments.
Develop agent orchestration and tool integrations using Model Context Protocol (MCP), tool calling, agent frameworks, and workflow engines such as Temporal.
Develop and integrate agents with the Actionable Observation store, Overwatch state machine and prioritization capabilities, and operator console.
Integrate agents with GitLab pipelines, GitLab CI/CD, JFrog Artifactory, canary deployment workflows, and Tenable Vulnerability Management (UVM) as a vulnerability-finding source.
Expose and consume enterprise knowledge graphs containing data lineage and environment context through MCP, ensuring agents have governed, read-only access to relevant information.
Implement event processing, data storage, and state management using Kafka, MongoDB, SQL, and SQLite.
Develop and query knowledge graphs using Neo4j and Cypher to support contextual retrieval, dependency analysis, and remediation planning.
Implement automated code-change workflows, AI-assisted code generation, test generation, and lockdown-test practices to validate remediation changes before deployment.
Apply context engineering and specification-driven development to improve agent accuracy, consistency, traceability, and maintainability.
Embed human-approval gates, audit trails, access controls, and AI governance controls throughout agent design, execution, and deployment.
Ensure autonomous workflows comply with enterprise security standards, change-management processes, deployment controls, and production-readiness requirements.
Support vulnerability remediation and software version-currency initiatives using CVE, EPSS, CVSS, End-of-Life (EOL), and End-of-Support (EOS) considerations.
Implement controlled deployment patterns, including canary releases and incremental rollouts, to reduce operational risk.
Troubleshoot agent execution failures, integration issues, workflow exceptions, and deployment problems; perform root-cause analysis and implement corrective actions.
Monitor and improve agent reliability, performance, scalability, and observability using Dynatrace or comparable tools where applicable.
Collaborate with application development, infrastructure, cybersecurity, DevSecOps, architecture, and production support teams to integrate agents into existing workflows.
Document reusable agent patterns, technical designs, integration approaches, governance controls, onboarding procedures, and operational best practices.
Coach application, infrastructure, and security teams on agent onboarding, secure usage, human oversight, and adoption of the autonomous operating model.
Participate in technical design reviews, code reviews, automated testing, release planning, and continuous improvement initiatives.
Own AI agent solutions across requirements, design, development, integration, testing, deployment, monitoring, and ongoing enhancement.
Basic Qualifications:
Minimum 7+ years of experience in software engineering or production application development, delivering and supporting enterprise-grade solutions.
Minimum 1–2+ years of hands-on experience developing LLM-based applications, Generative AI solutions, AI agents, or agentic workflows.
Minimum 3+ years of hands-on experience developing production software using Python and/or Java.
Minimum 3+ years of experience with Git, GitLab CI/CD, automated testing, and software delivery practices.
Minimum 1+ year of hands-on experience with AI agent orchestration, MCP, tool calling, and agent frameworks or equivalent technologies.
Minimum 1+ year of experience with agent development platforms such as AWS Agent Core, Kiro, or equivalent technologies.
Minimum 1+ year of experience with workflow orchestration engines such as Temporal or equivalent platforms.
Minimum 2+ years of experience developing automated code-change workflows, code validation, automated test generation, or lockdown-test practices.
Minimum 1+ year of practical experience applying context engineering and specification-driven development to AI or software engineering workflows.
Minimum 2+ years of experience working with event-processing or data-storage technologies such as Kafka, MongoDB, SQL, or SQLite.
Minimum 1+ year of experience with Neo4j, Cypher, graph databases, or knowledge-graph querying.
Minimum 3+ years of experience integrating applications with CI/CD pipelines, artifact repositories, deployment platforms, or enterprise tools.
Minimum 2+ years of experience implementing secure software engineering practices, access controls, audit trails, or human-in-the-loop approval mechanisms.
Minimum 1+ year of experience implementing AI governance, security guardrails, or operational controls for AI-driven automation.
Minimum 3+ years of experience with SDLC, automated testing, change management, release processes, and production support.
Minimum 3+ years of experience troubleshooting complex application or integration issues, conducting root-cause analysis, and implementing corrective actions.
Minimum 2+ years of experience collaborating with cross-functional teams, including application development, infrastructure, DevOps, or cybersecurity.
Minimum 1+ year of experience documenting technical designs, reusable engineering patterns, operational procedures, or implementation standards.
Minimum 1+ year of experience providing technical guidance, mentoring engineers, or coaching teams on engineering practices or new technology adoption.
Demonstrated ability to explain personal technical contributions, agent architecture, implementation decisions, challenges, and business outcomes from previous projects
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.
See All 47 Remote AI Agent Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote AI Agent Engineer JobsRemote AI Agent Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Education
- Insurance
- Automotive
- Media & Entertainment
What Employers Look For
The qualifications that appear most often in remote AI agent engineer jobs.
- Proficiency building agents with LLM orchestration frameworks such as LangChain, LangGraph, or AutoGen
- Experience designing and deploying tool-use and function-calling pipelines in production environments
- Strong Python skills with hands-on work in prompt engineering and context management
- Familiarity with RAG architectures, vector databases, and retrieval-augmented system design
- Ability to build evaluation frameworks that measure agent accuracy, reliability, and failure modes
- Bachelor's degree in computer science, AI, or a related engineering field, or equivalent experience
Tips for Your Remote AI Agent Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote ai agent engineer openings from across the U.S. in one place, so you can find roles that match your stack and apply directly. Remote postings fill fast, so checking regularly and applying to good fits right away gives you a real advantage.
Show agent projects on GitHub before you apply
Remote employers can't observe you in an office, so your public code does the talking. Push documented projects that demonstrate tool-use, retrieval-augmented generation, or multi-step reasoning chains so hiring managers can evaluate your work before the first call.
Prove async communication skills in your application
Remote ai agent engineering teams run on written communication. Write a crisp, specific cover note that explains your architecture decisions clearly. If you write the way you'd communicate in a Slack thread, you'll stand out to managers who need engineers who can collaborate without meetings.
Target remote-first companies over remote-optional ones
Remote-first companies build their entire engineering workflow around distributed collaboration, which means clearer async processes and no second-class-remote experience. Look for signals like public engineering blogs, open-source tooling, and distributed leadership teams when evaluating where to apply.
Remote AI Agent Engineer Jobs: Frequently Asked Questions
How do I get a remote ai agent engineer job?
Remote ai agent engineer roles go to candidates who can demonstrate self-direction and communicate clearly in writing, since there is no manager looking over your shoulder. Remote employers screen for hands-on experience with LLM orchestration frameworks, retrieval-augmented generation, and tool-use patterns. A public GitHub portfolio showing agent projects, strong async communication habits, and comfort shipping autonomously all give you a clear edge.
Which companies hire remote ai agent engineers?
Companies hiring remote ai agent engineers right now include ServiceNow, University of San Diego, and QUO, based on current remote listings on Migrate Mate as of November 2026. Remote-first software firms, AI infrastructure startups, and distributed enterprise technology teams make up the bulk of employers in this space.
Can you get a remote ai agent engineer job with no experience?
Yes, but remote entry-level roles are harder to land because employers expect you to operate independently from day one without on-site mentorship. Early-career candidates can open doors by contributing to open-source agent frameworks, publishing documented personal projects on GitHub, and targeting AI startups that explicitly hire junior engineers remotely. Showing you can debug and ship without hand-holding matters more than a resume line.
Do you need a degree for remote ai agent engineer jobs?
Not always. Remote employers in this field weigh practical skills heavily: demonstrated fluency with agent orchestration tools, real shipped projects, and clear written explanations of technical decisions often carry more weight than a formal credential. A strong portfolio, verifiable open-source contributions, and evidence of independent problem-solving can substitute effectively for a traditional computer science degree.
Which industries hire the most remote ai agent engineers?
Remote ai agent engineer roles concentrate in Technology & Software, Education, and Insurance, based on current remote listings on Migrate Mate as of November 2026. These sectors hire remotely because their engineering teams are already distributed and agent systems integrate directly into cloud-native workflows that require no physical presence.
See All 47 Remote AI Agent Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote AI Agent Engineer Jobs