GG10.2 Role Value Proposition MLOps Engineer plays a critical role in the Data & Analytics life cycle. The role requires one to demonstrate expertise in CI/CD automation, cloud platforms, containerization, and operational monitoring. It is an individual contributor role, expected to independently function Experience 5-8+ years of relevant experience Education Bachelors degree in computer science, information technology or equivalent educational qualification Responsibilities
Design, implement, and maintain deployment pipelines for batch and real-time models using on-prem and Azure cloud
Embed security best practices in deployments to ensure compliance with enterprise, governance, and regulatory standards.
Implement observability frameworks (logging, monitoring, alerting) to ensure reliability and performance.
Optimize deployments for performance, scalability and reliablity
Manage infrastructure for data pipelines and ML workloads across on-premise and cloud environments.
Deploy and manage containerized workloads using Docker and Kubernetes.
Drive adoption of best practices in DevOps and MLOps across the Data & Analytics ecosystem.
Document pipelines, environments, and operational standards for cross-team use.Design, implement, and maintain CI/CD pipelines for data, analytics, and ML solutions.
Independently lead design, solutioning & estimations
Collaborate with multiple partners from Business, Technology, Operations and D&A; capabilities (Data Governance, Data Quality, Data Modeling, Data Architecture, Data science, DevOps, BI & insights) Technical Skills
Deployment tools (Azure DevOps, Tosca,YAML Scripting, code versioning tools)
Big Data Frameworks : Apache Spark, Hadoop
Azure cloud services ( Synapse, Databricks, Azure Apps, AKS)
Containerization and orchestration (Docker, Kubernetes, )
Protocols/technologies like HTTP, SSL, LDAP, JDBC, SQL, XML
Python, ML model serving and monitoring frameworks (MLflow, Azure ML)
Shell Scripting : Powershell, bash, CMD, CLI
Res
📌 DevOps Engineer II (Pune)
🏢 MetLife
📍 Pune