DevOps Engineer II

Position Summary MetLife established a Global capability center (MGCC) in India to scale and mature Data & Analytics, technology and operations capabilities in a cost-effective manner and make MetLife future ready. The center is integral to Global Technology and Operations with a focus to protect & build MetLife IP, promote reusability and drive experimentation and innovation. The Data & Analytics team in India mirrors the Global D&A team with an objective to drive business value through trusted data, scaled capabilities, and actionable insights. The operating models consists of business aligned data officers- US, Japan and LatAm & Corporate functions enabled by enterprise COEs- data engineering, data governance and data science 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 Job 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) Knowledge, Skills And Abilities Education Bachelor’s degree in computer science, information technology or equivalent educational qualification Technical Skills And Experience 5-8+ years of relevant experienceDeployment 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 Rest APIs, Postman Communication skills, analytical skills, structured problem-solving skills Partner, Stakeholder engagement experience Good To Have (Preferred) DevOps certificationInfrastructure access management ( RBAC, Keyvault, CyberArk) Exposure to Gen AI technology and tools