Senior Specialist, Devops Engineer

Merck Merck · Pharma · Telangana, India

Senior Specialist, DevOps Engineer responsible for building and maintaining MLOps workflows, CI/CD pipelines, and cloud infrastructure (AWS, Kubernetes) for generative AI applications, including LLM-based authoring tools, machine translation, RAG pipelines, and embedding services. The role focuses on operational excellence, reliability, security, and compliance in a regulated healthcare environment.

What you'd actually do

  1. Design, implement, and maintain robust CI/CD pipelines (GitHub Actions or equivalent) to support continuous delivery of applications across the DRD product line.
  2. Provision, manage, and optimise cloud infrastructure on AWS using Infrastructure-as-Code (IaC) tools such as Terraform or AWS CloudFormation.
  3. Build and maintain MLOps workflows for generative AI applications — including model versioning, CI/CD for models, inference infrastructure, and monitoring for model drift and performance degradation.
  4. Implement and maintain observability stacks — logging, metrics, tracing, and alerting — to ensure operational visibility across all DRD applications and services.
  5. Collaborate with the CDDS Architecture team to ensure infrastructure and delivery practices align with enterprise architectural standards, guiding principles, and the Technology Assessment process.

Skills

Required

  • DevOps
  • CI/CD
  • GitHub Actions
  • AWS
  • Terraform
  • CloudFormation
  • Kubernetes
  • Docker
  • MLOps
  • model versioning
  • inference infrastructure
  • monitoring
  • observability
  • logging
  • metrics
  • tracing
  • alerting
  • security best practices
  • secrets management
  • role-based access control (RBAC)
  • vulnerability scanning
  • enterprise security standards
  • React.js
  • Node.js
  • Python/FastAPI

Nice to have

  • Veeva Vault

What the JD emphasized

  • regulated environment requirements
  • GxP
  • GxP-aware

Other signals

  • Generative AI applications
  • MLOps workflows
  • LLM-based agents
  • RAG pipelines
  • embedding services
  • model versioning
  • inference infrastructure
  • monitoring for model drift
  • performance degradation