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Who we are Lemrock builds the infrastructure of agentic commerce. Tomorrow, consumers won't just buy on e-commerce websites. They will discover, compare and purchase directly inside conversational AI interfaces like ChatGPT, Perplexity, and a growing long tail of specialized AI assistants. For brands, this is a major shift: if they are not present in these environments, they become invisible. Our mission: enable any brand to exist, sell and distribute in this new channel. We raised a €7M seed round (the first in Europe on this market), with Jean-Baptiste Rudelle, founder of Criteo, on our board, and the Bpifrance DeepTech label. Today, Lemrock is already: 100M+ conversations per month 150+ brand clients a $100B market structuring at high speed Context You'll join a tight-knit team with high product standards and direct access to production deployment : our models are tested in real-world conditions, with several million users each month, with very short cycles between prototyping, integration and deployment. In this context, you'll work on the next generation of agentic and recommendation systems : building the pipelines, agents, and models that sit at the core of our product. Your Mission Your goal is to build and scale the agentic infrastructure that powers Lemrock's commerce intelligence : turning raw signals into automated, self-improving systems at production scale. Concretely, you will: Analyze large-scale conversational interaction datasets (100M+ events/month) to uncover behavioral patterns, intent signals, and performance drivers. Design and deploy agentic pipelines end-to-end , from data ingestion and enrichment to model orchestration, monitoring, and continuous improvement, integrated into systems exposed to millions of requests daily. Build autonomous agents that keep our knowledge infrastructure accurate and current. Translate insights into iteration loops in production , by updating, fine-tuning, and improving our existing recommendation and ranking algorithms with tight constraints on latency, robustness, and business outcomes. Design and train new recommendation models from scratch when needed, with a focus on scalability, evaluation rigor, and deployability in real-world traffic. , Langchain) and/or native SDKs like OpenAI/Anthropic Observability and evaluation for LLM/agent workflows Vector search, scoring, prompting, LLM orchestration Hybrid recommender systems, causal inference, probabilistic models Bonus (appreciated but not required) TypeScript Engineering: Docker, GCP, PostgreSQL, Redis, Vector DBs, PostHog CI/CD Why Join Us Join a company in early breakout mode , already generating revenue, with a clear technological edge and strong financial backing to fuel its growth Work directly with an experienced team : two repeat YC founders who've scaled product & tech before, a former strategy/innovation director (10 years in the sector), and ex-strategy consultants (McKinsey QuantumBlack, BCG) Be part of the next structural shift of the web : agentic interfaces Own mission-critical topics , grow fast, and shape the company's future trajectory Package Competitive salary disclosed during the interview process.

AI Engineer

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AI Engineer

No apliques sin prepararte