MLOps Engineer — Real-Time AI Platform (Edge/Cloud)

Alta Ares; Paris; Sur place; EUR 70 000 - 90 000; Plein temps

Résumé du poste

Alta Ares in Paris is seeking an experienced MLOps Engineer to own the Gamma Ulixes AI platform, collaborating with ML, software and infra teams to turn research prototypes into production systems. You will build and scale the ML lifecycle from data ingestion to deployment, design reproducible workflows, experiment tracking, and model registries, and deploy across cloud, on‑premise and edge environments.

The role emphasizes ownership, pragmatism and a strong focus on reliability and developer

Qualifications

• 3–5+ years building production ML systems.
• Strong Python software engineering skills.
• Production-grade ML infrastructure expertise.
• Experience with Docker, Linux and containerized environments.
• Experience with ML experiment tracking, model registries and reproducible workflows.
• Experience designing APIs and backend services.
• Familiarity with CI/CD and cloud-native development.
• Strong ownership mindset and cross-team collaboration.
• Ability to drive projects from design to production.

Responsabilités

• Develop and improve Gamma Ulixes AI platform for training, evaluation and deployment.
• Build experiment tracking, model registry and dataset management capabilities.
• Design reproducible ML workflows and automated training pipelines.
• Develop benchmarking, evaluation and model validation services.

Connaissances

Python

Experience 3–5+ years

ML Platform design

API design

CI/CD

Cloud-native development

Ownership mindset

Collaboration across teams

Outils

Docker

Linux

Experiment tracking

Model registries

Reproducible workflows

APIs & backend services

Description du poste

Alta Ares in Paris is seeking an experienced MLOps Engineer to own the Gamma Ulixes AI platform, collaborating with ML, software and infra teams to turn research prototypes into production systems. You will build and scale the ML lifecycle from data ingestion to deployment, design reproducible workflows, experiment tracking, and model registries, and deploy across cloud, on‑premise and edge environments.

The role emphasizes ownership, pragmatism and a strong focus on reliability and developer