AI Engineer
Summary
Build and deploy production-grade generative AI systems (RAG, agents, extraction) for real users, including on-site customer work and handling strict data constraints.
Commit is hiring a GenAI Engineer to join a team building and delivering real-world generative AI solutions — systems that move beyond prototypes and into production, where real users rely on them every day.
You'll take complex problems from the first customer conversation through to a working system running in their environment. You'll build GenAI solutions that are deployed, used, and relied upon — not just demos or experiments.
The problems are unusual, and the constraints are real: on-premise and air-gapped deployments, strict data handling requirements, and complex customer environments. A meaningful part of your work will happen on-site with the people who use the systems you build.
What you'll do
- Build end-to-end GenAI systems: RAG pipelines, agentic workflows, document understanding, and extraction from messy real-world data.
- Own the full lifecycle — from data pipelines and model/prompt design to evaluation, deployment, monitoring, and the critical work of making systems reliable and production-ready.
- Define what success looks like for each use case, measure performance, and stand behind the results.
- Work directly with customers on-site, translating their needs into practical technical solutions.
Requirements
What we're looking for
- 3+ years building data or ML systems, with things you shipped that other people then used.
- Strong Python and real software engineering habits — testing, version control, code you'd hand to someone else.
- Hands-on experience with LLM-based systems: RAG, agents, structured extraction, or evaluation.
- Comfort with production: containers, CI/CD, cloud (AWS / GCP / Azure).
- Ability to explain technical work clearly to non-technical stakeholders.
Bonus: on-premise or disconnected deployment, defense or another regulated domain, classical ML depth.