Senior DevOps Engineer

About the Role

Our client is looking for a DevOps Engineer to build and scale the infrastructure behind our AI initiatives. You'll work at the intersection of platform engineering, cloud infrastructure, and machine learning — partnering closely with data scientists and software engineers to ship AI models reliably and efficiently.

This is a hands-on role with real ownership: you'll design the systems, automate the pipelines, and mentor the engineers who keep them running.

What You'll Do

• Build and own infrastructure — design, implement, and manage scalable, secure, and highly available systems that power AI workloads.
• Enable AI delivery — collaborate with data scientists and software engineers to streamline how models are deployed, monitored, and operated in production.
• Automate everything — drive deployment, monitoring, and scaling of AI applications using Kubernetes, Docker, and Terraform.
• Own the release pipeline — build and maintain CI/CD pipelines that make code releases fast, repeatable, and error-free.
• Keep systems healthy — monitor performance, troubleshoot issues, and continuously optimize for cost and efficiency.
• Grow the team — mentor junior DevOps engineers and champion best practices and emerging technologies.

What We're Looking For

Must-have:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in DevOps or a comparable role.
• Hands-on proficiency with cloud platforms such as AWS or Azure .
• Strong experience with containerization and orchestration — Docker and Kubernetes .
• Solid working knowledge of CI/CD tools such as Jenkins or Azure Pipelines .
• Familiarity with Infrastructure as Code — Terraform or Ansible .
• Exposure to GitOps workflows.
• Programming experience in Python , Go , or Shell scripting .
• Strong problem-solving skills and comfort operating in a fast-paced environment.
• Excellent communication skills and the ability to collaborate across cross-functional teams.

Nice-to-have:

• Experience with MLOps tooling or AI/ML deployment pipelines.
• Familiarity with observability stacks (Prometheus, Grafana, etc.).

What We Offer

• Work on real AI infrastructure at scale — not side projects.
• A collaborative, engineering-driven culture.
• Clear growth path with mentorship opportunities in both directions.