Project Manager Integration, DevOps & ML Engineer Cloud

Project overview

Context of the mission

Within the DSI's Technical Infrastructure Pole, the AI / MLOps squad ensures the industrialization, deployment, security, and operational maintenance of Artificial Intelligence solutions (Machine Learning, Computer Vision, GenAI/LLM) on multi-cloud environments (AWS, Azure, and GCP).

To support our projects, we are looking for an experienced Project Manager, Integration, DevOps & ML Engineer. Acting at the interface between Business / Data Science teams and Infrastructure / Operations teams, the contractor will form a close technical partnership with an existing ML/MLOps engineer to ensure service continuity and evolve two sets of ML use cases in production.

Set 1 - ML use cases on AWS (Applied Data Science & Serverless)

Set 2 - MLOps use cases on Azure (Model Industrialization)

• Context and purpose of the mission
• Operational scope
• Two ML use cases in production
• Large-scale operational data processing: exploratory data analysis (EDA) on noisy industrial data, design and maintenance of spatio-temporal matching algorithms (non-1:1 relationships, "closest match" disambiguation, time windows, handling missing data and faulty GPS signals).
• Serverless services and document generation: design and operation of serverless stream processing and automated document generation (e.g., multi-image reports).
• AWS technical environment: Amazon SageMaker (training, inference, pipelines, model registry), Amazon Bedrock (GenAI, RAG, Guardrails), Lambda, S3, Glue, Kinesis / Firehose, Athena, Comprehend / Textract, boto3; ELK and BigQuery data foundations; Terraform IaC.
• End-to-end model industrialization: versioned dataset management -> training pipelines -> automated evaluation gate -> model registry -> blue/green deployment -> production drift monitoring.
• Multi-site deployment and optimization: industrial multi-site architecture (factorized common foundation + per-site configuration), including reinforcement learning models (RL / SAC).
• Azure technical environment: Azure Machine Learning (managed online/batch endpoints, pipelines, model registry, MLflow, data assets, blue/green, drift), Microsoft Fabric / OneLake (industrial data foundation, medallion architecture), Azure DevOps (ML CI/CD pipelines), Azure AI (Azure OpenAI, AI Search, Cognitive Services), AKS, Terraform / Bicep IaC.

Project details

• Start: flexible
• Remote: hybrid
• Location: Paris, France, FR
• Rate: 74-74 EUR/h
• Languages: French