LLM Operations Engineer

We are looking for an LLM Ops Engineer with deep Databricks experience to build, automate, and scale our machine learning delivery pipelines on the Lakehouse. You’ll own the model lifecycle end‑to‑end—from data ingestion and feature engineering to CI/CD, deployment, monitoring, and governance—ensuring our ML systems are reliable, auditable, secure, and cost‑efficient.

You will partner closely with Leadership, Data Engineers, and subject matter experts to product ionize models using Databricks (Delta Lake, Unity Catalog, MLflow, Feature Store, Workflows) and modern Dev Ops practices across our cloud environments.

• Design and maintain Databricks work spaces, clusters, SQL Warehouses, cluster policies, and workspace governance (RBAC, SCIM, SSO, secret scopes).
• Implement robust data pipelines with Delta Lake (ACID tables, Z‑ordering, OPTIMIZE/VACUUM), Delta Live Tables (DAGs, expectations), and Workflows (jobs, task orchestration).
• Set up Unity Catalog for cross-workspace governance: data & model lineage, permissions, catalogs/schemas, data tags, and auditability.
• Operationalize ML models using MLflow (tracking, artifacts, metrics, model registry, approvals, stages: Staging/Production).
• Build/maintain Feature Store entities and feature pipelines; enforce reproducibility and feature governance.
• Establish model deployment patterns (batch scoring, streaming, microservices) using Model Serving.
• Create scalable CI/CD for notebooks, repos, and jobs using Azure Dev Ops, including unit/integration tests, data/feature validation, and registry promotions.
• Implement data quality and ML quality controls (e.g., Great Expectations/Delta expectations, statistical tests, drift detection, canary releases).
• Build robust monitoring & alerting for data freshness, pipeline SLAs, model performance, drift, and operational metrics.
• Optimize performance and cost (autoscaling, spot instances, D  runtimes, caching, storage tiers).
• Enforce compliance and security best practices (PII handling, encryption at rest/in transit, network controls, secret management).
• Partner with data engineers and subject matter experts to standardize templates for experiments, pipelines, model packaging, and deployment.
• Document patterns and build internal tooling (CLI utilities, Python packages) to streamline model release and observability.
• Contribute to incident response, post‑mortems, and continuous improvements.

• Operationalize ML models using MLflow (tracking, artifacts, metrics, model registry, approvals, stages: Staging/Production).
• Build/maintain Feature Store entities and feature pipelines; enforce reproducibility and feature governance.
• Establish model deployment patterns (batch scoring, streaming, microservices) using Model Serving.
• Create scalable CI/CD for notebooks, repos, and jobs using Azure Dev Ops, including unit/integration tests, data/feature validation, and registry promotions.
• Implement data quality and ML quality controls (e.g., Great Expectations/Delta expectations, statistical tests, drift detection, canary releases).
• Build robust monitoring & alerting for data freshness, pipeline SLAs, model performance, drift, and operational metrics.
• Operationalize ML models using MLflow (tracking, artifacts, metrics, model registry, approvals, stages: Staging/Production).
• Build/maintain Feature Store entities and feature pipelines; enforce reproducibility and feature governance.
• Establish model deployment patterns (batch scoring, streaming, microservices) using Model Serving.
• Create scalable CI/CD for notebooks, repos, and jobs using Azure Dev Ops, including unit/integration tests, data/feature validation, and registry promotions.
• Implement data quality and ML quality controls…