General Motors
AI Agent Engineer
Warren, Michigan, United States of America +1 · Hybrid · Full-time · Posted Jul 24, 2026
- Location
- Warren, Michigan, United States of America
Austin, Texas, United States of America - Experience
- 3–7+ years of professional software engineering or AI/ML engineering experience.
- Education
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; or equivalent practical experience.
- Visa sponsorship
- GM does not provide immigration-related sponsorship for this role; applicants who need GM sponsorship now or in the future should not apply.
About the job
Designs, builds, integrates, and operates AI agents that automate and augment enterprise knowledge work and improve employee productivity and decision-making.
What you'll do
- Develop agent workflows, orchestration, routing, and guardrails; harden prototypes for reliable production use.
- Integrate agents with LLM services, enterprise systems, APIs, and data sources.
- Monitor and evaluate agent behavior, maintain feedback loops, document designs, and promote responsible AI practices.
What they ask for
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; or equivalent practical experience.
- 3–7+ years of professional software engineering or AI/ML engineering experience in complex enterprise environments.
- Proficiency in at least one modern programming language; hands-on experience with LLM applications, AI frameworks, or agent tooling.
- Experience building production services with observability and testing; understanding of prompt engineering, RAG, RESTful APIs, data structures, algorithms, and basic ML/LLM concepts.
Nice to have
- Experience with Glean or similar enterprise AI search/assistant platforms.
- Experience with Microsoft 365 or enterprise collaboration ecosystems; familiarity with IAM, AI observability, A/B testing, or UX collaboration.
Tools
Python, TypeScript/JavaScript, Java, LangChain, Semantic Kernel, Glean, M365, Copilot
AI focus
Build and operationalize LLM-powered agents and workflows, using RAG, embeddings, vector search, evaluation, monitoring, and responsible AI safeguards.
Model-written summary · the original posting is authoritative