ML Ops Engineer
This listing is for a ML Ops Engineer role at Signal Processing Engineer (RF Processing & Analytics), in Paris, France. The listing describes it as a full-time position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
This listing is for a ML Ops Engineer role at Signal Processing Engineer (RF Processing & Analytics), in Paris, France. The listing describes it as a full-time position.
The advert names Python as a skill for this role.
The employer does not state a salary in this listing; DGJ Insight below shows an official pay benchmark for the place where one exists.
Benefits mentioned in the advert: bonus.
Visa sponsorship is not mentioned. Applicants from outside France should check the official work permit route before applying.
Dev Global Jobs found this job on Arbeitnow on Nov 2, 2026.
- Employer
- Signal Processing Engineer (RF Processing & Analytics)
- Location
- Paris, France
- Job type
- Full-time
- Pay
- Not stated by the employer
- Found
- Nov 2, 2026 on Arbeitnow
- Employer text
- 417 words, about 2 min to read
- Apply only on the official page (arbeitnow.fr). Dev Global Jobs is not the employer and never asks for a fee.
- Mirror the skills the advert names (Python) in your CV where you really have them; applicant tracking systems look for these words.
- Read the employer’s full text below (about 2 min) before you apply.
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DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
UN living-cost index for Paris (ICSC post adjustment 50.5), as of 1 Oct 2026.
Source: ICSC post adjustment classifications (icsc.un.org)
- Talent Passport: Qualified employees, researchers, artists, investors; multi-year permit
- Employee (salarié) visa: Employer work authorisation from the labour office
- Temporary worker visa: Contracts under 12 months
About the role
Full text from the employer, as published on Arbeitnow, arranged into sections. Nothing added or removed.
01About Us
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
02ABOUT THE ROLE
Our ML teams build and optimize the models at the core of our autonomy stack. As a team scales, the workflow the models run on needs to move from a manual, organically grown setup onto a controlled, automated, and reproducible footing.
As an MLOps Engineer, operating out of Paris, Lausanne, or Zurich, you will own that machinery, across training and evaluation pipelines, CI, experiment tracking, and reproducibility, so that models are trained, benchmarked, and compared in a controlled and repeatable way. You free the modelers to focus on models rather than infrastructure, and you set the standard the team builds on.
03RESPONSIBILITIES
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.
04CANDIDATE REQUIREMENTS
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.
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.
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