AI Agent Engineer
About The Position
We build the AI layer for Starburst's products, including AIDA. We design agents that let users ask questions in natural language and get accurate, grounded answers backed by their actual data. We operate with startup speed inside an enterprise company, shipping weekly and measuring results. This is the first dedicated agent engineering hire on the team. You will raise the bar on agent reliability, turning shipped features into systems users depend on. You will approach AI agents as production software systems, built with the same rigor as any large-scale backend platform. This is not a research role or a prototyping role. You will ship agentic features that work with messy real-world data, undocumented schemas, and diverse access patterns. You have already shipped agentic or LLM-powered systems at an AI startup or AI-focused product team. You bring proven patterns, knowledge of real failure modes, and operational lessons that would take the team months to learn through trial and error. You write code every day and ship every week.
Requirements
- 5+ years building AI/ML systems, with at least 1 year shipping agentic or LLM-powered systems in production
- Direct experience at an AI startup or AI-focused product team (not consulting, integration, or agency work)
- Hands-on experience with agent frameworks (LangChain, LangGraph, CrewAI, Semantic Kernel, or custom)
- Production RAG pipeline experience (chunking strategies, vector databases, embedding models, reranking)
- Strong software engineering fundamentals: testing, observability, CI/CD, code review discipline
- Track record of shipping frequently and iterating on user feedback
Nice To Haves
- Experience with enterprise data systems (SQL engines, data catalogs, schema metadata)
- Experience building evaluation and observability pipelines for agentic systems
- Familiarity with JVM-based systems or polyglot development environments
- Contributions to open-source agent frameworks or tooling
- Experience with function calling, tool use patterns, and context window management
Responsibilities
- Design and build agent architectures: tool orchestration, planning, memory, multi-step reasoning, error recovery
- Ship production-quality agent features with clear ownership from design through deployment
- Close the gap between demo and production: handle partial schemas, inconsistent metadata, mixed data formats, and unreliable source systems
- Introduce proven patterns for agent reliability: structured error handling, fallback chains, observability, guardrails
- Build evaluation suites that catch hallucinations, regressions, and grounding gaps before they reach users
- Collaborate with the AI Research Engineer to integrate grounding and retrieval into agent workflows
- Drive technical standards for agent development through code, not documents
- Turn improvements into measurable gains in task completion rate, accuracy, and time-to-value
Benefits
- competitive pay
- attractive stock grants
- flexible paid time off
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
