Senior DevOps Engineer

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