Job description

A brand-new Innovation Hub and AI Lab needs an ML platform built from the ground up, one that lets the Data Science and AI team scale experimentation and move faster from prototype to production, and that is exactly what this MLOps Engineer role delivers. You will design and automate robust ML pipelines for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of models. Model serving architectures get configured to support both ultra-low latency real-time APIs, using tools such as FastAPI and gRPC, and large-scale batch inference. Monitoring and logging systems get implemented to track model metrics, runtime latencies, data drift, and concept decay once models reach production. Model registries and metadata get managed to keep ML artifacts reproducibly versioned, tracked, and auditable, using tools such as MLflow and Kubeflow. Containerized applications get orchestrated and scaled using Docker and production-grade Kubernetes or managed container services such as EKS and AKS. Security and data teams become close collaborators, enforcing strict data governance, pipeline encryption, network isolation, and secure IAM policies across the platform.