ML OPS Engineer

Role Overview

ML Ops Engineer responsible for building and running end-to-end automation of the machine learning lifecycle, from training through deployment and retraining.

Responsibilities

• Design, implement, and manage end-to-end ML lifecycle automation (training, validation, deployment, and retraining pipelines)
• Develop and maintain CI/CD pipelines to support integration testing and deployment of ML models into production
• Deploy, monitor, and manage ML models in production, ensuring availability, scalability, and high-performance inference services
• Implement model monitoring, logging, and tracking to detect issues such as data/model drift and to trigger automated retraining
• Collaborate with data engineers, data scientists, and platform teams to streamline data pipelines and ensure reliable data flow for ML workflows
• Ensure governance, versioning, and traceability of ML artifacts in line with enterprise security, compliance, and audit needs

Requirements

• Azure Machine Learning
• Industrial AI - Machine Learning (ML) / MLOps-related capabilities (as listed)
• Python
• ThoughtMachine

Nice-to-haves

• AI/ML Testing
• CI/CD Architecture
• MLOps and Model Life Cycle Management