Since day one, DataGalaxy has been guided by a simple conviction: data creates value when people align on it, adopt it, and turn it into business outcomes. Metadata is not the destination. It is the foundation that makes this possible. That’s why DataGalaxy built the value governance platform, a business-first approach that connects data and AI strategy to execution, IT teams to business teams, and metadata to business outcomes. The platform includes two core products: DataGalaxy Catalog and DataGalaxy Portfolio Center. Catalog is a metadata center to provide context, build trust, and ensure compliance. Portfolio Center is a product management tool to show value creation from initiatives and champion alignment from C-levels to business stakeholders. Founded in France and expanding rapidly across Europe and the United States, DataGalaxy is trusted by over 200 global enterprises, including Dior, Airbus, and SwissLife. The company is committed to driving data culture and literacy by helping organizations deliver metadata to the agents and value to the people.Our values: Be intentional. Be clear. Be bold. Be humble.

About the role

Join the AI-Share team to help build and operate the foundations that power our Generative AI features (LLM, RAG, agents) inside the DataGalaxy data governance platform. This role focuses on MLOps / ModelOps delivery: making GenAI capabilities reliable in production (deployment, monitoring, cost control, traceability), while collaborating with product engineering teams across a polyglot stack.

You don’t need to match every item below - we value curiosity, eagerness to learn, pragmatism, and steady progress!

What you’ll do (with support from senior engineers)

MLOps / ModelOps (core)

• Contribute to the evolution of our ModelOps platform for GenAI: provider integrations, configuration, deployment automation, and operational tooling.
• Help implement practical patterns for running GenAI workloads in production: evaluation, versioning, reproducibility, safe rollouts/rollbacks, and environment management.
• Build and improve CI/CD workflows adapted to AI: packaging, automated checks, evaluation steps (when applicable), deployment, and rollback.
• Improve traceability of AI assets (configs, prompts/templates when applicable, evaluation outputs, versions) to support governance and debugging.
• Add and maintain observability for GenAI workloads: latency, availability, usage/cost signals, and quality-related indicators (dashboards/alerts).

GenAI feature development & platform integration (core)

• Develop and improve GenAI features within the platform (agent, RAG pipelines, MCP server): new capabilities, prompt engineering, bug fixes, and client-facing improvements.
• Work closely with Product / Data / Engineering to integrate GenAI capabilities into the platform in a maintainable way.
• Participate in code reviews, documentation, and post-incident follow-ups (RCA / action items), with guidance from the team.

Tech environment (high level)

• Python for MLOps tooling, evaluation, automation, and integrations
• Cloud services and managed GenAI providers (e.g., Azure AI Foundry, AWS Bedrock, GCP Vertex)
• CI/CD, containers (Docker), observability tooling
• A polyglot product stack (e.g., backend services and front-end surfaces owned by other squads)