Lead LLM Engineer

Licorne Society a été missionné par une startup IA en pleine croissance pour les aider à trouver leur Lead LLM Engineer.

What you will own

You will be responsible for one thing:
Make our AI outputs reliable, fast, and indispensable in real workflows.
Concretely:

• Design and evolve our LLM / agent architecture


• Own output quality across key use cases (emails, document analysis, etc.)


• Build evaluation systems (datasets, metrics, regression detection)


• Drive fast iteration loops from production data


• Improve retrieval, reasoning, and tool usage


• Ensure production reliability (latency, failure modes, fallback)


• Work directly with product + founders on what to build and why

What this role is really about

Most teams fail because:

• they don’t know what “good output” means


• they don’t have evals


• they iterate randomly


• they overuse agents

Your job is to fix that.
You will turn:

• vague user problems


• → into structured AI systems


• → with measurable performance


• → that improve every week

What you need to be excellent at

1. Shipping real LLM systems

• You’ve built systems used in production (not demos)


• You understand RAG, tools, agents, structured outputs


• You can design full pipelines, not just prompts

2. Evaluation-driven development

• You know how to define quality metrics


• You build datasets from real usage


• You run continuous evals to prevent regressions

3. Debugging complex failures

• You can trace issues across:




• retrieval


• prompts


• model behavior




• You don’t guess — you isolate and fix

4. Speed of iteration

• You move from problem → improvement in hours or days, not weeks


• You use logs, traces, and data — not intuition alone

5. Strong judgment

• You know when to:




• use an agent vs a pipeline


• add complexity vs simplify




• You optimize for reliability and user value, not novelty

What we don’t care about

• Number of years of experience


• Whether you’ve used a specific framework


• Fancy research credentials

If you can build, debug, and improve real systems, you’re a fit.

What success looks like (first 90 days)

• Clear eval framework for core use cases


• Measurable improvement in output quality


• Faster iteration cycles across the team


• Reduced hallucinations / failures


• Stronger system architecture decisions

Stack (context, not requirements)

• Python (FastAPI)


• Postgres


• Google Cloud


• LangGraph / LangChain (evolving)


• PostHog (product analytics)


• Langfuse (LLM traces)


• LLM APIs (Azure OpenAI)