Devops Engineer

 

Position Summary

 

MetLife established a Global capability center (MGCC) in India to scale and mature Data & Analytics, technology capabilities in a cost-effective manner and make MetLife future ready. The center is integral to Global Technology and Operations with a 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

 

DevOps 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. This role mentors ML Ops engineers. It is an individual contributor role, expected to solve wide ranging business problems.

 

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 reliability

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

Mentor ML ops engineers

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)

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Education

Bachelor’s degree in computer science, information technology or equivalent educational qualification

 

Experience

(In Years)

8-10+ years of relevant experience

 

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

Rest APIs, Postman

Communication skills, analytical skills, structured problem-solving skills.

Storytelling, Partner, Stakeholder engagement experience

Mentorship experience

 

Preferred skills

DevOps certification

Infrastructure acess management ( RBAC, Keyvault, CyberArk)

Exposure to Gen AI technology and tools

Banking Financial Services and Insurance domain knowledge