Senior MLOps Consultant (Databricks)

Set up, maintain, and optimize Databricks environments including clusters, policies, and workspaces for ML pipelines and workloads.

Build and manage CI/CD pipelines to promote notebooks, jobs, workflows, and models across environments using Git, GitHub Actions, Jenkins, or Azure DevOps.

Deploy, monitor, and troubleshoot ML models and workflows on Databricks with tools like MLflow, ensuring production reliability and cost efficiency.

6–12 years of experience in ML Engineering, MLOps, or platform engineering with hands-on Databricks experience.

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

Strong expertise with Databricks components: workspaces, clusters, policies, Jobs and Workflows, Repos, Delta Lake, Spark (batch & streaming), and MLflow.

Proficiency in Python plus working knowledge of PySpark or Spark SQL; experience with CI/CD tools like GitHub Actions, Jenkins, or Azure DevOps; and experience deploying ML models on cloud platforms (Azure, AWS).

Experienced in managing end-to-end ML lifecycle on Databricks, including environment setup, CI/CD, model deployment, and operational monitoring.

Capable of translating Data Scientist notebooks into scalable, reproducible production pipelines with robust automation and governance.

Familiar with healthcare or pharmaceutical data domains and working in cross-functional teams involving data scientists, ML engineers, and platform teams.