Freelance Senior AI Platform Expert - Azure Databricks

We are looking for a

n experienced freelancer to advise and support our internal platform team in designing, building and improving an AI platform on Azure Databricks. The role is internal only, with no client-facing responsibilities.

The freelancer will act as a senior technical reference for the team, helping shape the architecture, establish engineering standards, review technical implementations and support the team in building and operating a reliable, scalable and secure platform. The ideal candidate combines strong hands-on technical expertise with the ability to mentor engineers and transfer knowledge.

The freelancer will support the team across the design, implementation and continuous improvement of the platform, including:

• Defining and reviewing the target architecture on Azure Databricks, covering workspaces, environments (dev/test/prod), Unity Catalog, security, networking and integration with Azure services.
• Advising on CI/CD/CT pipelines for code, data and models, including automated testing, promotion across environments, model registry workflows and continuous training triggered by schedules, drift or new data.
• Reviewing architecture decisions, code, Terraform configurations and pipeline implementations, providing actionable recommendations and identifying areas for improvement.
• Defining reusable MLOps standards, templates and engineering best practices.
• Supporting platform reliability, scalability, governance, security and operational excellence.
• Contributing hands-on when needed to unblock the team, troubleshoot technical challenges or develop reference implementations.
• Coaching team members, facilitating design and code reviews, and transferring knowledge to help the team become increasingly autonomous.

Requirements

• Extensive experience in data engineering, ML engineering, platform engineering or MLOps, ideally with at least 8 years of relevant experience.
• Strong practical experience with Azure Databricks in production environments, ideally at least 3 years.
• Solid knowledge of MLOps and the Databricks ecosystem, including MLflow, model registry, Model Serving, Feature Engineering, Databricks Workflows and monitoring.
• Experience designing and implementing CI/CD/CT pipelines, ideally using GitLab, and infrastructure-as-code practices.
• Hands-on experience with Terraform and cloud infrastructure, ideally using the Databricks and Azure providers.
• Solid knowledge of Azure services, including ADLS Gen2, Key Vault, Entra ID and cloud networking (VNet injection, private endpoints).
• Strong Python and PySpark skills, alongside software engineering best practices such as testing, packaging and code quality.
• Experience with Unity Catalog, including data governance, lineage and access control.
• Proven ability to mentor engineers, conduct design and code reviews, and establish technical standards.
• Experience with GenAI/LLMOps on Databricks, including technologies such as Vector Search, Mosaic AI Agent Framework and AI Gateway, is a strong plus.
• Experience implementing secure, private-network cloud environments and working with sensitive data is highly valued.