Senior MLOps Engineer — Data Platform (Azure/Databricks)

Atos; Brussel Hoofdstad; Hybride; EUR 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

Platform-as-a-product culture

Autonomy over projects

Résumé du poste

Atos is seeking a Senior MLOps Engineer to own and advance the data platform’s ML lifecycle. You will work on a production-grade MLOps stack using Databricks and MLflow, deliver end-to-end pipelines, and collaborate with domain teams to scale across the organization.

You will own architecture, governance, and automation, building reusable templates and tooling. The role requires hands-on engineering, strong Python, and deep Cloud / Infra experience in Azure and Terraform.

Qualifications

• 5+ years in MLOps, ML engineering or platform engineering with production models.
• Hands-on Databricks and MLflow expertise is essential.
• Strong Python engineering and API/tooling skills.

Responsabilités

• Own the MLOps capability end to end, including design, implementation and roadmap.
• Assess and refine the platform alignment for ML lifecycle, landing zones, governance and automation.
• Deliver an incrementally adoptable ML lifecycle for one domain, then scale to all domains.
• Implement lifecycle on Databricks and MLflow: tracking, registry, feature tables, packaging, serving and monitoring.
• Build CI/CD promotion across environments for reproducible, auditable model releases.
• Use off-the-shelf capabilities where possible and custom components where needed.
• Enable automated retraining, drift detection and Lakehouse Monitoring with alerting and rollback paths.
• Productionise batch and near real-time inference.
• Treat models as data products with contracts, SLOs, lineage and cost signals.
• Provide cost visibility for ML workloads and right-size compute.

Connaissances

Python engineering

MLOps

ML engineering

Outils

Databricks

MLflow

Unity Catalog

Model Serving

Lakehouse Monitoring

Terraform

Azure

Description du poste

Atos is seeking a Senior MLOps Engineer to own and advance the data platform’s ML lifecycle. You will work on a production-grade MLOps stack using Databricks and MLflow, deliver end-to-end pipelines, and collaborate with domain teams to scale across the organization.

You will own architecture, governance, and automation, building reusable templates and tooling. The role requires hands-on engineering, strong Python, and deep Cloud / Infra experience in Azure and Terraform.