Senior DevOps Engineer

Role Overview

Key Responsibilities

• Design, build, and maintain AWS, GCP, or Azure infrastructure using IaC.
• Architect and manage CI/CD pipelines for applications and ML model deployment.
• Build Kubernetes clusters and manage containerized services and ML inference workloads.
• Own logging, metrics, tracing, alerting, and observability tooling.
• Support ML lifecycle infrastructure, including feature stores, model registries, training pipelines, and GPU clusters.
• Manage GPU provisioning and optimize compute costs.
• Drive IAM, secrets management, network policy, and compliance security practices.
• Lead incident response, on-call rotations, postmortems, and SRE culture.
• Mentor junior engineers and collaborate with data science and ML teams.
• Manage multi-region and multi-cloud deployments, disaster recovery, capacity planning, and FinOps.

Required Skills and Experience

• Deep expertise in at least one hyperscaler: AWS, GCP, or Azure; working knowledge of a second is required.
• Experience with compute, networking, storage, IAM, infrastructure as code, automation, containers, and CI/CD.
• Experience with observability, security, Python automation, databases, and messaging systems at scale.
• Terraform, Pulumi, CloudFormation, Ansible, Chef, Puppet, Docker, Kubernetes, Helm, and Kustomize are listed tools.
• Jenkins, GitLab CI, GitHub Actions, ArgoCD, CircleCI, Prometheus, Grafana, ELK/EFK, and Datadog are listed tools.

Preferred Qualifications

• AWS, GCP, or Azure Solutions Architect certification and CKA/CKAD are preferred.
• Experience running LLM inference infrastructure at scale is preferred.
• Experience optimizing costs for large-scale GPU training is preferred.
• Contributions to open-source infrastructure or MLOps tooling are preferred.
• BE/BTech, Information Technology, or a related discipline.