ML Ops Engineer, Chanakya

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.

You will work across both layers: supporting Strategic Deployment Engineers in the field, and owning the model deployment infrastructure for new products being built by the product engineering team. The standards here are uncompromising — a model failure is not a UX problem, it is an operational risk.

What You'll Do

• 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

What We're Looking For

• 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

Signals We Look For

• You've kept a production ML system running under load — and debugged it when it broke
• You don't wait for things to fail; you build systems that tell you when they're about to
• You write documentation that actually gets used, by people who aren't you

Who You Are

• You treat uptime and correctness as equally non-negotiable
• You understand that operational reliability is a form of trust-building
• You're as comfortable optimising inference throughput as you are writing a field runbook for a deployment engineer
• You take ownership of model and stack health across every active deployment — not just the ones you set up