MLOps Engineer_MLOps platform Squad

Responsibilities

• Design, deploy, and operate production Kubernetes clusters (on-premises) with focus on high availability, scalability, and zero-downtime deployments
• Manage Helm charts and ArgoCD for GitOps-based continuous delivery of platform and application workloads
• Build and maintain CI/CD pipelines in Azure DevOps for automated testing and deployment across data and ML services
• Architect and maintain a modern data lake using open table formats (Iceberg) on on-premises and hybrid storage
• Build, schedule, and monitor scalable batch and streaming pipelines using Apache Airflow, Apache Spark, and Apache Flink
• Deploy and operate Trino and ClickHouse as query engines for analytics over the data lake
• Deploy and operate JupyterHub and Dagster on Kubernetes for ML experimentation and workflow orchestration
• Manage the Feature Store (Feast / PostgreSQL) and Parquet-based data storage for ML workloads
• Work closely with Data Science and Data Engineering teams to productionize ML workflows and onboard new data sources

Languages