Role Summary The MLOps Engineer will build and run components of the organisation's AI platform under the MLOps Lead: model-serving deployments on GPUs, AI gateway configuration, CI/CD pipelines with evaluation gates, the model registry, monitoring, and fine-tuning jobs.

Educational Qualification

- B.Tech./B.E., M.Tech./M.S. or M.Sc. in Computer Science, Information Technology, AI/ML or a related discipline (Must have)
- Certifications (desirable, not mandatory): CKA or CKAD, a cloud DevOps certification
- Candidates from non-traditional backgrounds with robust verifiable work will be considered in lieu of formal qualification

Experience

- 3+ years in DevOps, platform or MLOps engineering (5+ years preferred); minimum 1 year deploying and operating ML or LLM models in production
- Demonstrable deployment of at least one ML or LLM model to production by the candidate, with monitoring in place
- Experience with Kubernetes in production, infrastructure as code and CI/CD
- Experience with GPU workloads (drivers, CUDA, GPU Operator) is a strong plus
- Experience serving LLMs with vLLM or SGLang is a plus

Key Responsibilities Model Deployment & Serving

- Deploy and scale models on Kubernetes with vLLM, SGLang or NVIDIA Triton across development, staging and production, managing versions, canary releases and rollback

CI/CD for AI
- Build GitLab CE pipelines with tests,



evaluation gates and adversarial-test gates; package models and services; deploy with Argo CD

Gateway & Registry Operations
- Configure gateway routes, keys, quotas and fallbacks for self-hosted and external providers, and maintain model and prompt registry entries with provenance

Monitoring & Observability
- Maintain dashboards, alerts and runbooks for latency, errors, GPU utilisation, token usage and cost, including LLM tracing

GPU & Training Jobs
- Schedule fine-tuning and batch jobs on shared GPUs, and maintain drivers, base images and environments with the GPU infrastructure engineer

Security, Audit & Support
- Manage secrets and access, pin dependencies and scan images, maintain deployment logs for audit, and take part in on-call and post-incident reviews

Technical Competencies
- Containers & Orchestration: Docker, Kubernetes, Helm; KServe or Ray Serve (desirable)
- Serving: vLLM, SGLang or NVIDIA Triton; ONNX Runtime
- Gateways: LiteLLM, Kong AI Gateway or equivalent
- CI/CD & Infrastructure as Code: GitLab CE CI or GitHub Actions; Argo CD; Terraform or OpenTofu, Ansible
- ML Lifecycle: MLflow, DVC
- Monitoring: Prometheus, Grafana, Loki, OpenTelemetry; Langfuse or Arize Phoenix; NVIDIA DCGM
- GPU: NVIDIA drivers, CUDA, NVIDIA Container Toolkit, GPU Operator
- Security: HashiCorp Vault or OpenBao; Trivy or Grype; Cosign
- Core: Python, Bash, YAML; Linux administration

📌 MLOPs Engineer (Delhi)
🏢 National e-Governance Division (NeGD), Digital India
📍 Delhi