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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in MLOps and platform engineering, with a strong focus on Azure Databricks and CI/CD pipeline implementation. Capable of mentoring teams, establishing technical standards, and ensuring platform reliability and security.
Highest-signal resume keywords
Azure DatabricksMLOpsCI/CD PipelinesTerraformPython
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringML EngineeringPlatform EngineeringModel RegistryFeature EngineeringDatabricks WorkflowsCloud InfrastructureSoftware Engineering Best PracticesUnity CatalogGenAI/LLMOps
Soft Skills
MentoringCoachingKnowledge TransferDesign ReviewsCode Reviews
Tools & Technologies
GitLabAzure ServicesADLS Gen2Key VaultEntra IDVNet InjectionPrivate EndpointsVector SearchMosaic AI Agent FrameworkAI Gateway
Industry Keywords
Cloud NetworkingData GovernanceOperational ExcellenceSecurityScalability
Tech Stack
Tools & technologiesAzureCloudPySparkPythonTerraformUnityVault
About the role
Key responsibilities & impact- Advise and support the internal platform team in designing, building and improving an AI platform on Azure Databricks.
- Define and review target architecture covering workspaces, dev/test/prod environments, Unity Catalog, security, networking and Azure integrations.
- Advise on CI/CD/CT pipelines for code, data and models, including automated testing, environment promotion, model registry workflows and continuous training.
- Review architecture decisions, code, Terraform configurations and pipeline implementations; provide actionable recommendations.
- Define reusable MLOps standards, templates and engineering best practices.
- Support platform reliability, scalability, governance, security and operational excellence.
- Contribute hands-on to unblock the team, troubleshoot technical challenges and develop reference implementations.
- Coach team members, facilitate design and code reviews, and transfer knowledge to improve team autonomy.
Requirements
What you’ll need- 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.