Lead DevOps Engineer (AI & GPU)
Jobgether (company site, opens in a new tab)8+ yrsRemoteDirect career pageEst. 28–45 LPA
Posted 20h ago
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Job description
Structured overview of role & requirements
About This Role
- Lead the design and operation of CI/CD and GitOps pipelines for production-grade AI services across cloud, on-premises, edge, and air-gapped environments.
- Manage and optimize GPU-based AI inference infrastructure, including observability, resource utilization, cost attribution, and governance for scalable AI operations.
- Collaborate with platform, security, and engineering teams to integrate security, compliance, and continuous verification into AI workload delivery and runtime management.
Minimum Requirements
- Bachelor's or Master's degree in Computer Science or related technical discipline.
- At least 8 years of professional experience in DevOps, MLOps, platform engineering, or related fields with production ownership of AI workloads or high-throughput services.
- Experience with CI/CD pipeline design, GitOps tools (Argo CD, Helm, GitHub Actions, GitLab CI), and production-scale AI inference frameworks (Ray Serve, vLLM, NVIDIA Triton, NVIDIA NIM) on GPU infrastructure.
- Ability to operate infrastructure across cloud, on-premises, edge, and air-gapped environments with strong security and compliance focus.
Ideal Candidate Profile
- Senior engineer with deep expertise in AI infrastructure, GPU workload optimization, and DevOps/MLOps automation for high-throughput inference.
- Experienced in multi-environment deployment and observability platforms integrating telemetry (Prometheus, OpenTelemetry) and cost management (FinOps).
- Proven ability to lead cross-functional collaboration defining operational contracts and integrating security, compliance, and progressive deployment strategies in AI production environments.