Senior MLOps/Data Engineer

What you'll do

In a nutshell, own the end-to-end ML lifecycle on Azure and Databricks, working with applied scientists to operate reliable models.

• Orchestrate and maintain ML pipelines (ingest → feature engineering → train → evaluate → deploy→monitor→repeat) on Azure + Databricks
• Standardize experimentation using MLflow or similar tools (tracking, artifacts, model registry, stages)
• Automate jobs with Databricks Workflows and CI/CD (GitHub Actions or Azure DevOps)
• Implement data & model observability: freshness/completeness, drift (features/model), training/serving skew, SLA/SLO monitoring
• Ensure security & compliance
• Handle incidents and post-mortems for ML pipelines and serving infrastructure

What you'll need

• Excellence in Python software engineering and developing tests
• Fundamental understanding of Machine Learning
• 5+ years in Data Eng/MLOps roles
• Strong PySpark
• Hands-on with Databricks and Delta Lake
• CI/CD for data/ML (Git, PR workflow, automated tests, environment pinning)
• Azure basics
• Monitoring and building dashboards
• Clear communication; operational-excellence mindset (SLA/SLO ownership)

What's nice to have

• Unity Catalog experience
• Databricks Feature Store
• Terraform for workspace/clusters/jobs/UC objects
• Telemetry domain exposure
• Optimize PySpark jobs (partitioning, caching, etc.) and cost (autoscaling, spot).