Senior DevOps/MLOps

About the Role

We are seeking an experienced DevOps Engineer (6+ years) with deep expertise in cloud infrastructure, container orchestration, automation, and MLOps.

You will play a key role in building and scaling cloud-native platforms while supporting ML/AI workloads, including large language models (LLMs) such as OpenAI and Claude.

This position requires close collaboration with engineering and data science teams to ensure secure deployments, efficient CI/CD workflows, and optimized cloud resource management across AWS (primary), with additional workloads on Azure and GCP.

Key Responsibilities

• Cloud Infrastructure: Design, implement, and manage multi-cloud environments using Terraform and Helm charts.
• Kubernetes: Deploy, configure, and maintain clusters (EKS, AKS, GKE) with GitOps workflows via ArgoCD.
• Containers: Build, secure, and optimize Docker workflows, including GPU-enabled images (CUDA, Nvidia drivers) for ML/AI workloads.
• CI/CD Pipelines: Develop and optimize pipelines using Azure DevOps (primary) and GitHub Actions; integrate GitOps practices with ArgoCD.
• MLOps: Support workflows for deploying and scaling LLMs (OpenAI, Claude, or similar) and integrating ML models into production.
• Monitoring & Reliability: Implement monitoring, logging, and alerting systems (Prometheus, Grafana, ELK).
• Collaboration: Partner with developers and data scientists to deliver secure, scalable, and reliable deployments.
• Required Skills & Experience
• 6+ years as a DevOps / Cloud Engineer.
• Strong AWS expertise: EKS, ECR, managed services
• Production-grade Kubernetes with Helm charts, Karpenter, and ArgoCD.
• Advanced Terraform for complex environments.
• Proven CI/CD experience: Azure DevOps pipelines (primary) and GitHub Actions.
• Deep knowledge of Docker, secure builds, and performance optimization.
• Familiarity with Nvidia GPU driver integration and ML/AI container optimization.
• Hands-on MLOps: ML model deployment and scaling LLMs.