MLOps Engineer: Reproducible AI Pipelines

Harmattan AI

Zürich

On-site

CHF 90,000 - 150,000

Full time

14 days+

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Job summary

Harmattan AI seeks an MLOps Engineer (Zurich/Lausanne/Paris) to own the training and evaluation pipelines, CI, and reproducibility. You will enable modelers to focus on models by standardizing infrastructure and tooling.

The role emphasizes responsible, scalable ML in a defense context, with cross-location collaboration and production-grade ML workflows. The candidate will contribute to a secure, auditable pipeline environment, enhance experiment tracking, and promote reusable templates while

Qualifications

  • Degree in a STEM field or equivalent practical experience.
  • Experience building ML pipelines, CI, and experiment tracking.
  • Proficiency in Python and infrastructure software.
  • Bonus: experience with on-device or hardware-in-the-loop testing.
  • Attributes: systematic, reliability-minded, and service-oriented.
  • Commitment to Harmattan AI's mission of defensive, high-impact tech.

Responsibilities

  • Build and maintain reproducible training and evaluation pipelines for models.
  • Introduce CI to ML work and ensure runs are comparable across code, config, and models.
  • Maintain a model lifecycle registry from sandbox to production.
  • Keep results traceable and comparable through experiment tracking and logging.
  • Wire on-device and hardware-in-the-loop testing into automation and collect results.

Skills

Python
CI/CD
ML pipelines
Experiment tracking
Software engineering

Education

STEM degree

Tools

Model Registry

Job description

Harmattan AI seeks an MLOps Engineer (Zurich/Lausanne/Paris) to own the training and evaluation pipelines, CI, and reproducibility. You will enable modelers to focus on models by standardizing infrastructure and tooling.

The role emphasizes responsible, scalable ML in a defense context, with cross-location collaboration and production-grade ML workflows. The candidate will contribute to a secure, auditable pipeline environment, enhance experiment tracking, and promote reusable templates while

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