Senior DevOps Engineer

Cornerstone

Senior DevOps Engineer

Hyderabad, India

req11535

We're looking for a Senior DevOps Engineer This role is Office Based, Hyderabad Office

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 Responsibilities

• AWS & 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.

• Containerize applications and develop Kubernetes manifests/Helm charts.

• Design appropriate Kubernetes architecture, networking, ingress, scaling, and deployment strategies.

• Ensure minimal service disruption during migration and establish operational best practices on EKS.

• Self-Hosted LLM & AI Infrastructure

• Deploy and manage self-hosted Large Language Models and inference services.

• Work with model serving frameworks such as vLLM.

• Design containerized infrastructure for GPU-based model serving and inference.

• Manage model versions, deployments, configurations, and rollback strategies.

• Support migration of ML services from managed APIs/services to self-hosted models.

• Work with engineering teams on API integration and inference infrastructure.

• Databricks Administration

• Administer Databricks workspaces, clusters, permissions, and access controls.

• Manage cluster configuration, policies, and resource utilization.

• Support LMI Insights and related ML/AI workloads.

• Troubleshoot Databricks infrastructure and connectivity issues.

• Implement appropriate security and access-control practices.

• Elasticsearch Infrastructure

• Design, deploy, and manage Elasticsearch clusters.

• Perform cluster sizing, scaling, configuration, and performance optimization.

• Manage indices, mappings, retention, and data lifecycle requirements.

• Support Kibana configuration, dashboards, and troubleshooting.

• Monitor Elasticsearch health, capacity, and performance.

• Monitoring, Reliability & Auto-Scaling

• Implement monitoring and observability for Kubernetes, AWS, and ML services.

• Use Prometheus, Grafana, and AWS CloudWatch for monitoring and alerting.

• Configure Kubernetes HPA/VPA and other auto-scaling mechanisms.

• Establish proactive alerting for infrastructure and application health.

• Perform capacity planning and resource optimization.

• Identify opportunities for AWS infrastructure and compute cost optimization.

• Infrastructure as Code & Automation

• Build and maintain infrastructure using Terraform.

• Develop reusable Terraform modules for AWS and Kubernetes infrastructure.

• Manage Kubernetes deployments using Helm charts.

• Automate infrastructure provisioning, configuration, deployments, and operational tasks.

• Maintain infrastructure documentation and deployment standards.

• Cross-Team Collaboration

• Partner closely with Data Engineering, ML Engineering, Data Science, and Software Engineering teams.

• Understand data pipelines, SQL, APIs, and ML service architecture sufficiently to troubleshoot end-to-end workflows.

• Coordinate infrastructure requirements for new ML models and services.

• Participate in production incident resolution, root-cause analysis, and continuous improvement.

• Establish engineering standards around deployment, monitoring, security, and operational ownership.

• You've got what it takes if you have...

• Required Skills & Experience

• 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.

• Preferred / Nice-to-Have Skills

• Experience supporting Generative AI / LLM platforms.

• Experience with GPU infrastructure and NVIDIA/CUDA environments.

• Experience with model lifecycle and model version management.

• Experience migrating workloads between managed cloud services and Kubernetes.

• Experience with AWS networking such as VPC, load balancers, security groups, and Route 53.

• Experience with API gateways and microservice architectures.

• Experience with Python or shell scripting for infrastructure automation.

• Experience working with Data Engineering and ML teams in a production environment.

• What You'll Own

• AWS ML/AI infrastructure

• EKS cluster administration and upgrades

• ML service deployment and production operations

• CI/CD automation

• App Runner → EKS migration

• Self-hosted LLM infrastructure and vLLM

• Databricks platform administration

• Elasticsearch infrastructure

• Monitoring and auto-scaling

• Terraform and Helm-based infrastructure automation

• Cloud cost, reliability, and performance optimization

• Ideal Candidate

• The ideal candidate is a hands-on infrastructure engineer who can independently take an ML/AI service from containerization → CI/CD → AWS infrastructure → EKS deployment → monitoring → scaling → production support.

• They should be comfortable working across both traditional DevOps infrastructure and modern AI/ML infrastructure, and should be able to collaborate closely with Data Engineering and ML teams while taking ownership of the underlying platform.

• LI-Onsite

Our Culture:

• Spark Greatness. Shatter Boundaries. Share Success. Are you ready? Because here, right now – is where the future of work is happening. Where curious disruptors and change innovators like you are helping communities and customers enable everyone – anywhere – to learn, grow and advance. To be better tomorrow than they are today.

Who We Are:

• At Cornerstone, we believe in AI that works in the service of people, amplifying their judgment to drive high-performing, future-ready organizations forward. Cornerstone Workforce AI™, the intelligence platform for