What a MLOps Engineer actually does
You build feature stores, train-eval-deploy pipelines (Kubeflow, Airflow, MLflow, SageMaker, Vertex AI), serve models behind low-latency APIs (TorchServe, Triton, Ray Serve), instrument data and model drift, and own the SLOs of every production model.
Why MLOps Engineer matters in India right now
MLOps is one of the highest-paid sub-tracks in Indian tech in 2026. Senior MLOps roles at GCCs (JP Morgan, Goldman Sachs, Walmart, Adobe) and AI-natives now pay full parity with senior backend / SRE roles, with the talent pool an order of magnitude smaller than either.
Core competencies hiring panels expect
Python + at least one ML framework, deep Kubernetes (real cluster experience, not just kubectl), Docker, AWS or GCP, observability (Prometheus / Grafana / Datadog), and an opinion on at least one production-ML toolchain (Vertex AI vs SageMaker vs self-hosted).
Skill hubs to study before applying:
How seniority pays in 2026
Mid-level MLOps (3–6 yrs) earn ₹22–45 LPA. Senior MLOps clear ₹45–80 LPA. Staff-level at FAANG-tier crosses ₹1.2 cr. The compensation premium over equivalent DevOps is typically ₹6–12 LPA at mid-level.
Common reasons candidates self-eliminate
The most common interview failure is treating MLOps as DevOps + scripts. Strong candidates speak fluently about feature freshness SLAs, training/serving skew, shadow deployments, and the operational difference between batch and online inference.
Common questions
- MLOps vs DevOps vs SRE — what's the salary delta?
- At the senior level: DevOps and SRE are roughly comparable; MLOps carries a 10–25% premium over both at the same level in India, driven by the supply gap.
- Can I become an MLOps Engineer from a backend role?
- Yes, and it's the most common path. Add at least one ML framework, deep Kubernetes, one cloud's ML stack (Vertex AI / SageMaker), and one project where you owned a model in production end-to-end.