Our Company

At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

Location: Hybrid – Hyderabad, Pune and Bangalore, India

Job Summary

We are looking for a Senior DevOps Engineer to design, build, and operate scalable, reliable cloud-native infrastructure that supports modern applications, including platforms running LLM and Generative AI workloads. You will work closely with software engineers, ML/AI engineers, and product teams to enable fast, secure, and resilient deployments.

You’ll work closely with data scientists, ML engineers, platform teams, and product managers to operationalize AI/ML workflows, enabling smooth transitions from experimentation to production. You’ll also define test strategies, automate validation pipelines, and champion the overall quality of Teradata’s AI/ML platform and analytic products.

What You’ll Do

• Design, implement, and maintain Kubernetes-based platforms for production workloads.
• Build and manage containerized applications using Docker.
• Create, maintain, and version Helm charts for application and service deployments.
• Own CI/CD pipelines and deployment strategies (rolling, blue-green, canary, etc.).
• Improve system reliability, scalability, observability, and operational excellence.
• Partner with AI/ML teams to support LLM and GenAI workloads in production environments.
• Troubleshoot complex infrastructure and deployment issues across environments.
• Establish best practices for infrastructure automation, security, and cost efficiency.

Minimum Requirements

• 5+ years of experience in DevOps, SRE, or Platform Engineering roles.
• Strong hands-on experience with Kubernetes in production environments
• Proficient with Docker and container lifecycle management
• Solid experience building and managing Helm charts
• Deep understanding of deployment strategies and CI/CD pipelines
• Experience operating cloud-native systems (AWS, Azure, or GCP)
• Understanding of how LLM and Generative AI systems are deployed and operated.
• Familiarity with serving LLM models, inference workloads, or AI services in Kubernetes.
• Awareness of performance, scaling, and cost considerations for GenAI workloads.

 Preferred Qualifications

• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, or a related field (or equivalent experience).
• Experience supporting ML platforms, model serving, or vector databases.
• Knowledge of GPU scheduling, resource optimization, or high-performance workloads.
• Experience with observability tools (Prometheus, Grafana, OpenTelemetry, etc.
• Infrastructure as Code (Terraform, CloudFormation, etc.).

#LI-VB1