Senior Staff Engineer (DevOps Engineer- AI/ML)

Company Description

In a changing and evolving world, challenges are ever more unique and complex. Nagarro helps to transform, adapt, and build new ways into the future through a forward-thinking, agile, and caring mindset. Today, we are 18,000+ experts across 37+ countries, forming a Nation of Nagarrians, ready to help our customers succeed.

The nature of IT & digital product engineering has reached an incredible state of velocity and transition. We must adapt and meet it with an agile mindset that isn't afraid to iterate towards the perfect solution. If we only solve today's problems, it's not enough. We must do more. We must courageously embrace the future, with vision and clarity about where technology & business are heading. Thinking breakthroughs gets us there.

Nagarro - https://www.nagarro.com/en

Job Description

Job Description

Looking for a DevOps/MLOps Engineer to build and manage scalable, automated infrastructure for our LLM-powered GenAI platform. You’ll enable fast iteration and reliable deployment of models and services through robust CI/CD pipelines, container orchestration, and ML lifecycle tooling.

Key Responsibilities:

• Design and maintain CI/CD pipelines using Jenkins, GitHub Actions, or similar.
• Automate infrastructure provisioning using Terraform and manage services with Kubernetes.
• Write and maintain Bash/Python scripts for automation and operational tooling.
• Implement and monitor MLOps workflows using tools like MLflow, Azure ML, or similar.
• Support deployment and monitoring of LLM-based models and APIs in production.

Required Skills:

• Hands-on experience with Jenkins, GitHub Actions, or equivalent CI/CD tools.
• Proficiency with Terraform, Kubernetes, Docker, and cloud-native practices.
• Strong scripting skills in Bash and Python.
• Experience with ML model tracking, versioning, and deployment using MLflow or similar.
• Familiarity with cloud platforms (e.g., Azure, AWS, or GCP).

Nice to Have:

• Exposure to LLM/GenAI deployment workflows.
• Experience with model performance monitoring and observability tools (Prometheus, Grafana, etc.).
• Security and cost optimization best practices for ML infrastructure.