Job Description :

Role : Senior MLOps Engineer

Role Mandate : Build the technical controls, automation, and deployment pipelines that make the centralized MLOps operating model work in practice.

Key Responsibilities :

- Build CI/CD pipelines for controlled promotion from development/test to production using Azure DevOps or GitHub Actions.

- Implement MLflow tracking and Unity Catalog-based model registry and promotion patterns.

- Create reusable Databricks Jobs and Workflows for training, validation, registration, deployment, and retraining.

- Automate environment provisioning and controls using Terraform and Infrastructure as Code.

- Implement RBAC, service principals, managed identities, Key Vault integration, and production access restrictions.

- Build model serving, batch inference, monitoring, alerting, and rollback patterns.

- Implement usage logging, tags, compute policies, and cost telemetry for market and use-case attribution.

- Build guardrails so market users can work independently in sandbox and development environments without direct production deployment rights.

- Operationalize RAG and GenAI workloads with evaluation, observability, security, and cost controls