ML Ops Engineer
Paris, Lausanne, ZurichFull-timePosted 2mo ago
About the role
ML Ops Engineer responsible for building and maintaining reproducible training/evaluation pipelines, CI, experiment tracking, and model lifecycle tooling to enable modelers to focus on modeling in Harmattan AI's autonomous defense systems.
Responsibilities
- Training & Evaluation Pipelines: Build and maintain the reproducible training and evaluation pipelines the modelers run on, along with the pipeline templates and tooling they build on. The verdict on model quality stays with the modelers.
- CI & Reproducibility: Bring CI to ML work and make runs genuinely comparable across code, config, and models.
- Model Registry: Maintain a model lifecycle registry, from sandbox to production.
- Experiment Tracking & Logging: Keep results comparable and traceable across the team.
- Test Automation: Help wire on-device and hardware-in-the-loop test runs into automation and collect the results.
Qualifications
- Educational Background: A degree in a STEM field, or equivalent practical experience. Practical pipeline, CI, and reproducibility experience matters more than the specific degree.
- MLOps Experience: Built and maintained ML pipelines, CI, and experiment tracking in a real production or research setting, ideally taking a manual flow and making it controlled and reproducible.
- Engineering: Strong in Python and software engineering for infrastructure.
- Bonus: Experience wiring pipelines to on-device or hardware-in-the-loop testing.
- Attributes: Systematic, reliability-minded, and service-oriented so the team is enabled, pragmatic, and good at reducing friction.
- Commitment: 100% dedication to Harmattan AI's mission of providing a defensive edge to allied nations through ethical, high-impact technology.
Last updated Aug 12, 2026. Interested? Apply on the company site.
About Harmattan AI
Building the Future of Autonomous Warfare.
Building the Future of Autonomous Warfare.
Autonomous WarfareUASPayloadCounter-UAS
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