MLOps Engineer: Build Reproducible ML Pipelines & CI

Harmattan AI; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Harmattan AI is seeking an MLOps Engineer to own the training and evaluation pipelines, CI, experiment tracking, and a model registry. You will enable modelers by automating workflows and ensuring reproducibility across runs, configurations, and platforms.

The role is based in Paris, with opportunities in Lausanne or Zurich, and requires strong Python and ML pipeline expertise to support scalable autonomous defense systems.

Qualifications

• Educational Background: A degree in a STEM field, or equivalent practical experience.
• MLOps Experience: Built and maintained ML pipelines, CI, and experiment tracking in a real production or research setting.
• 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.
• Commitment: 100% dedication to Harmattan AI's mission of providing a defensive edge to allied nations through ethical, high-impact technology.

Responsabilités

• 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.
• 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.

Connaissances

MLOps

Python

CI/CD

Experiment tracking

Infrastructure engineering

Formation

STEM degree

Description du poste

Harmattan AI is seeking an MLOps Engineer to own the training and evaluation pipelines, CI, experiment tracking, and a model registry. You will enable modelers by automating workflows and ensuring reproducibility across runs, configurations, and platforms.

The role is based in Paris, with opportunities in Lausanne or Zurich, and requires strong Python and ML pipeline expertise to support scalable autonomous defense systems.