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 3+ 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).