Software Engineer (Agentic AI)

Join Dotmatics, a leading provider of scientific informatics solutions. As a Software Engineer (Agentic AI), you will be responsible for building the AI agent at the core of our Luma platform. You will work in a small, high-trust engineering team and have a significant impact on the development of our AI technology.

- Développer l'agent lui-même : la boucle, le harnais et le flux de contrôle reliant le modèle, les outils et l'état, y compris les sous-agents, les tâches de longue durée, le point de contrôle et la récupération. - Pratiquer l'ingénierie contextuelle : décider de ce qui entre dans la fenêtre contextuelle, quand compacter ou résumer, et comment garder l'agent cohérent au cours d'expériences de longue durée. - Concevoir des outils pour l'agent, avec des interfaces et des descriptions claires, couvrant l'orchestration des instruments, la planification, la capture des données et les outils de développement.

- We are looking for people who have around 10 years of experience in software engineering, a minimum of 3 years in AI, including hands-on experience building LLM-powered agents in production. Ideally you will have a degree or higher education in Computer Science, Engineering, or a related technical field, and an interest in life sciences, lab instrumentation, or automation - Production engineering: Python with type checking, automated tests, and packaging in CI, plus Node.js/TypeScript and React services and interfaces, including streaming output, with monitoring, tracing, and managed configuration and secrets - Context engineering, memory, and retrieval: a long-running LLM application where you can explain what went into the window, when it was compacted, and how coherence and retrieval quality (RAG, vector indexes, reranking) were measured - Agent security: permissions, sandboxing, human approval gates, prompt injection defenses, and audit logging, tested against adversarial inputs - Tool and API design for models: tools or MCP servers built for model use, with tool-selection and call accuracy measured - Production agents: at least one LLM-powered agent shipped to real users, handling multi-step tool use, error recovery, and long-running tasks - AI-native engineering: autonomous coding agents (Cursor, Claude Code, Codex, or equivalent) as the primary way you build - Experience with agent frameworks such as LangGraph, or building custom harnesses from scratch - Working knowledge of Databricks and BI tools (Sigma, Power BI, Spotfire) - Observability for LLM systems (tracing, cost and latency monitoring) - Fine-tuning, prompt optimization, or model evaluation experience - Authored skills, hooks, or MCP servers that made agent-driven delivery repeatable for a team - We are open to speaking to candidates with more or less experience than listed