LLM Engineer

Leonar; Paris; Sur place; EUR 90 000 - 150 000; Plein temps

Résumé du poste

Leonar is building an AI coworker for legal and professional services, automating emails, document analysis, and complex workflows. The role focuses on owning and scaling a production AI system relied on daily, with direct collaboration with product and founders.

Your work will turn vague user problems into structured AI systems with measurable performance that improve weekly, addressing hallucinations and reliability in real-world workflows.

Qualifications

• Experience building production-grade LLM systems and architectures.
• Ability to design and implement evaluation frameworks and metrics.
• Strong debugging skills for complex model and system failures.
• Proven track record of shipping fast iteration loops from production data.
• Good technical judgment and decision-making under uncertainty.

Responsabilités

• Design and evolve the LLM/agent architecture for production use.
• Own output quality across key use cases (emails, document analysis, etc.).
• Build evaluation systems (datasets, metrics, regression detection).
• Drive fast iteration loops using production data and feedback.
• Improve retrieval, reasoning, and tool usage.
• Ensure production reliability (latency, failure modes, fallbacks).
• Collaborate with product and founders on what to build and why.

Connaissances

Shipping Real LLM Systems

Evaluation-Driven Development

Debugging Complex Failures

Speed of Iteration

Strong Technical Judgment

Outils

Python (FastAPI)

Postgres

GCP

LangGraph

LangChain

PostHog

Langfuse

Azure OpenAI

Description du poste

We are building the AI coworker for legal and professional services — automating emails, document analysis, and complex workflows in high-stakes industries.

We are not looking for someone to “use GPT APIs.” We are looking for someone to own and scale a production AI system that users rely on daily.

What You Will Own

You will be responsible for one primary outcome: making 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, fallbacks).
• Work directly with product and 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, lack proper evals, iterate randomly, or 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

• Shipping Real LLM Systems
• Evaluation-Driven Development
• Debugging Complex Failures
• Speed of Iteration
• Strong Technical Judgment

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)

• A clear evaluation framework established for core use cases.
• Measurable, quantitative improvement in output quality.
• Faster iteration cycles across the team.
• Significantly reduced hallucinations and failure modes.
• Robust, scalable system architecture decisions.

Tech Stack (Context, Not Strict Requirements)

• Language/Backend: Python (FastAPI)
• Database: Postgres
• Cloud: Google Cloud Platform (GCP)
• Orchestration: LangGraph / LangChain (evolving)
• Observability: PostHog (analytics), Langfuse (LLM tracing)
• LLM Infrastructure: Azure OpenAI / Multi-provider APIs