Quick answer: Deploy and monitor ML models in production.
A MLOps Engineer is the bridge between data scientists and production systems, responsible for deploying, monitoring, and maintaining machine learning models at scale. They ensure that models trained in notebooks become reliable, fast, and scalable applications serving real users. In India, companies like Razorpay, Swiggy, Flipkart, and government initiatives (NeGD) increasingly hire MLOps Engineers to operationalize AI systems across fintech, e-commerce, and digital governance. Unlike data scientists who focus on model accuracy, MLOps Engineers prioritize reliability, latency, cost efficiency, and continuous monitoring. The role sits at the intersection of machine learning, software engineering, and DevOps—combining infrastructure expertise with ML domain knowledge to solve the challenge of keeping models healthy in production.