Senior DevOps Engineer
- Compensation
- Not disclosed
- Location
- Hyderabad
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Devops
- Posted
Skills
Must have
- 5+ years of experience in DevOps, Cloud Infrastructure, SRE, or Platform Engineering.
- Strong hands-on experience with AWS.
- Strong experience administering Amazon EKS and Kubernetes in production.
- Hands-on experience with: AWS EKS AWS SageMaker AWS Bedrock AWS ECS AWS App Runner Kubernetes Docker NGINX / AWS ALB Ingress Cloudflare / Cloudflare Tunnels
- Strong experience with Terraform and Helm.
- Strong experience developing CI/CD pipelines using GitHub Actions and/or Azure DevOps.
- Strong YAML scripting and Git experience.
- Experience migrating CI/CD pipelines and repositories from Azure to AWS/GitHub.
- Experience deploying and operating ML/AI services.
- Experience with self-hosted LLM/model serving, preferably vLLM.
- Experience with GPU-based workloads is highly desirable.
- Experience with Databricks administration.
- Experience managing Elasticsearch and Kibana.
- Experience with Prometheus, Grafana, and CloudWatch.
- Strong understanding of Kubernetes HPA/VPA, networking, ingress, DNS, and service discovery.
- Strong understanding of cloud networking fundamentals.
- Experience with production troubleshooting, monitoring, capacity planning, and cost optimization.
- Strong understanding of security, IAM, secrets management, and access control.
Full description
Senior DevOps / Cloud Platform Engineer - ML & AI Infrastructure
Job Summary
We are looking for a Senior DevOps / Cloud Platform Engineer with strong experience in AWS, Kubernetes, CI/CD, infrastructure automation, and ML/AI infrastructure to design, deploy, manage, and optimize cloud infrastructure supporting our machine learning and AI services.
The ideal candidate will have hands-on experience with AWS EKS, SageMaker, Bedrock, Docker, Kubernetes, Terraform, Helm, GitHub Actions, Databricks, Elasticsearch, and self-hosted LLM deployments. This role will work closely with Data Engineering, Machine Learning, and Software Engineering teams to build reliable, scalable, secure, and cost-efficient platforms for ML services across development and production environments. In this role you will...
Key ResponsibilitiesAWS & Kubernetes Infrastructure
- Design, deploy, and administer AWS infrastructure supporting ML and AI workloads.
- Manage Amazon EKS clusters, including cluster provisioning, upgrades, scaling, networking, and troubleshooting.
- Work with AWS SageMaker, AWS Bedrock, EKS, ECS, and related AWS services.
- Configure and manage Kubernetes Ingress controllers such as NGINX and AWS ALB.
- Manage Cloudflare Tunnels, DNS, Cloudflare configuration, networking, and security.
- Troubleshoot application, networking, compute, and infrastructure issues across AWS and Kubernetes environments.
- Implement best practices for security, reliability, availability, and scalability.
CI/CD & Azure-to-AWS Migration
- Build and maintain CI/CD pipelines for ML and AI services.
- Develop and manage GitHub Actions and Azure DevOps pipelines using YAML.
- Migrate repositories and CI/CD workflows from Azure DevOps to GitHub/AWS.
- Automate build, test, containerization, deployment, and release processes.
- Establish deployment strategies across development, staging, and production environments.
ML Service Deployment
- Deploy and manage ML services across AWS EKS/ECS and SageMaker.
- Build and maintain Docker containers and Kubernetes deployments.
- Manage environment segregation and configuration across Dev, QA, and Production.
- Develop and maintain Kubernetes manifests and Helm charts.
- Troubleshoot ML service deployment, networking, scaling, and runtime issues.
App Runner to EKS Migration
- Lead migration of existing services from AWS App Runner to Amazon EKS.