Were hiring a hands on Lead AI Engineer to own our LLM/RAG and multi agent orchestration systems end to-end. Youll design, build, and ship production-grade pipelines that are accurate, reliable, and scalable and youll mentor a team of junior AI engineers (most are early career) to raise the engineering bar.

This is a high ownership role in a fast?moving startup environment: youll get ambiguous problems, define the approach, implement solutions yourself, and coordinate with others when needed.

What youll own :

- RAG platform quality: retrieval strategy, chunking, embeddings, reranking, grounding/citations, hallucination mitigation.

- Evaluation & reliability: offline/online eval harnesses, regression tests, quality gates, monitoring, fallbacks.

- Multi?agent orchestration: designing agent roles, tool use, workflows, state, memory, and coordination patterns.

- Team leadership: technical direction, code reviews, mentoring, and execution standards.

What youll build (examples) :

- Build and iterate on agentic AI systems that solve real customer and internal workflows.

- Deliver production-grade RAG pipelines with strong accuracy, grounding, and measurable improvement cycles.

- Develop reusable orchestration patterns (routing, tool-use, planning/execution loops, memory/state) that other engineers can extend.

Must-have qualifications :

- 5 to 8 years overall software engineering experience.

- 2+ years building production LLM/RAG systems (not prototypes): retrieval pipelines, prompting strategies, grounding, evaluation.

- Strong understanding of multi agent orchestration in production: tool calling, routing, planning/execution loops, structured outputs, reliability patterns.

- Proven ownership: can take ambiguous problems architecture implementation production.

- Experience leading or acting as a player coach (mentoring, raising bar, setting standards).

- Strong communication and collaborative problem solving.