MLOps Engineer

Job Description

As an MLOps Engineer, you will be responsible for operationalizing machine learning models and ensuring their seamless transition from development to production. You will design, implement, and maintain robust pipelines, infrastructure, and tooling that enable scalable, reliable, and secure AI / ML solutions. Working at the intersection of data science, software engineering, and operations, you will play a vital role in accelerating AI deployment while ensuring models remain effective and maintainable over time ..

Key Responsibilities

• Design, build, and manage CI / CD pipelines for ML model deployment in cloud and on-premise environments.
• Containerize and orchestrate ML workloads using Docker and Kubernetes.
• Integrate models into operational systems via APIs, event-driven workflows, or microservices.
• Implement model monitoring systems to track performance metrics, data drift, and prediction accuracy.
• Maintain model versioning and registries to ensure reproducibility and governance.
• Automate retraining, validation, and redeployment processes to keep models up to date.
• Work closely with Data Scientist to productionize experimental models.
• Partner with Data Engineers to design and optimize high-quality data pipelines feeding into ML models.
• Collaborate with DevOps and IT Security teams to ensure infrastructure compliance, scalability, and security.
• Optimize compute and storage resource usage for cost efficiency and performance.

Requirements