ML-OPS Engineer |

ROLE OVERVIEW

The person who ensures every model moves seamlessly from experimentation to reliable production.

The ML Ops Engineer is responsible for the deployment, monitoring, and lifecycle management of machine learning systems across all projects at The Developer Community. You will build and maintain ML pipelines, manage model training and versioning, deploy models to production, and ensure their performance, scalability, and reliability over time. This includes handling data pipelines, monitoring model drift, and continuously improving models based on real-world usage. Working closely with developers and ML engineers, you ensure that models move efficiently from experimentation to stable production systems. Nothing ML-powered goes live without passing through your function — you are responsible for making models production-ready and dependable. You will receive an ML Ops Engineer title, credited contributions on every application you work on, and a verified certificate of internship reflecting hands-on experience with real-world ML deployment and operations.

OPEN ROLES