ML Ops Engineer, Chanakya
DelhiFull-timePosted 3mo ago
About the role
The MLOps Engineer owns the model lifecycle across all defence and strategic sector deployments — from serving infrastructure and monitoring to evaluation pipelines and environment management. You ensure the system is always on, always accurate, and always auditable.
Responsibilities
- Design and operate model serving infrastructure across on-prem and cloud deployments
- Build and maintain CI/CD pipelines for model updates, rollbacks, and evaluation-gated deployments
- Monitor model performance in production — latency, accuracy drift, throughput, failure modes — and build systems that surface issues before clients do
- Build evaluation infrastructure: harnesses, A/B testing, and model comparison tooling for field and lab use
- Manage containerised model serving in constrained, air-gapped, and edge environments
- Collaborate with Data Scientists on eval pipelines; own the infrastructure layer underneath
- Create runbooks and operational playbooks that Strategic Deployment Engineers can use in the field
- Own incident response for model-layer failures across all active deployments
Qualifications
- 3–5 years in ML engineering or MLOps with at least one production LLM or ML system in continuous operation
- Deep expertise in model serving: vLLM, TGI, Triton Inference Server, or equivalent; experience with quantised model formats (GGUF, AWQ, GPTQ)
- Experience fine-tuning and adapting models in constrained, on-prem, or air-gapped environments, including managing data pipelines and compute limitations specific to the environment
- Containerisation experience with Docker, Kubernetes, or lightweight alternatives (K3s, K0s) for constrained and edge environments; familiarity with deploying across heterogeneous hardware and infrastructure configurations
- Monitoring and observability using Prometheus, Grafana, or equivalent; ability to build custom eval dashboards
- Python fluency; familiarity with fine-tuning workflows and model evaluation frameworks
- Hands-on experience with CI/CD tooling for ML pipelines: GitHub Actions, ArgoCD, DVC, or similar
Skills
Required:Model servingvLLM, TGI, Triton Inference ServerQuantised model formatsOn-prem / air-gapped environmentsContainerisationMonitoring and observabilityPythonCI/CD for ML
Last updated Jul 14, 2026. Interested? Apply on the company site.
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