Senior DevOps Engineer

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