[Hiring] Staff DevOps & MLOps Engineer @Grid Dynamics Holdings

Role Description

As a Staff Engineer, you will sit at the intersection of DevOps, MLOps, and distributed systems. You will lead the design, evolution, and reliability of a multi-GPU, cross-cloud platform that enables cutting-edge AI to function in real-world environments.

• Architecture & Leadership: Lead the design, evolution, and long-term technical direction of scalable, multi-GPU infrastructure and model training platforms across cloud environments (AWS, GCP, etc.).
• Scale & Optimization: Drive reliability, performance, and cost-efficiency at scale; optimize distributed training workloads (scheduling, resource utilization, observability).
• Automation & CI/CD: Build and evolve infrastructure-as-code and automation for provisioning, orchestration, and lifecycle management. Architect and improve CI/CD systems for both infrastructure and ML training workflows.
• Collaboration: Partner closely with ML engineers and researchers to enable efficient experimentation and seamless productionization.
• SRE & Troubleshooting: Lead the troubleshooting and resolution of complex system issues across distributed, GPU-heavy environments.
• Best Practices & Mentorship: Define and implement best practices for infrastructure, DevOps, and MLOps across the organization. Mentor engineers and raise the bar for overall engineering quality and operational excellence.
• Documentation: Document architecture, systems, and key technical decisions clearly.

Qualifications

• Production-grade, hands-on experience with Kubernetes (CKA certification is preferred).
• Production-grade, hands-on experience using Terraform.
• Experience with Kubernetes application packaging and release management using Helm.
• Hands-on experience operating heavy workloads on a major cloud provider (specifically AWS).
• Experience building and operating CI/CD pipelines, including self-hosted build runners (GitHub Actions).
• Deep hands-on experience with monitoring and alerting stacks (Prometheus and Grafana).
• Strong foundational knowledge in Linux administration, containerization, and container orchestration.
• Solid automation and scripting skills utilizing Python and Bash.
• Flexibility to participate in an on-call rota for urgent issues outside of regular business hours.

Requirements

• Hands-on experience operating GPU-accelerated Kubernetes clusters (NVIDIA).
• Experience with gang scheduling, resource allocation, and fair-sharing (queues and priorities) for large-scale ML training.
• Experience with cluster autoscaling/dynamic node provisioning (Karpenter) and high-performance shared storage systems (FSx for Lustre, EFS).

Benefits

• Opportunity to work on bleeding-edge projects.
• Work with a highly motivated and dedicated team.
• Competitive salary.
• Flexible schedule.
• Benefits package - medical insurance, sports.
• Corporate social events.
• Professional development opportunities.
• Well-equipped office.