Job Description - MLOps Engineer

Role Overview

MLOps Engineers build the systems and processes that allow AI models to be deployed, monitored and improved reliably.

The role combines DevOps, cloud infrastructure, machine learning lifecycle management and production operations.

Alternative Job Titles

Design CI/CD pipelines for model training, validation, deployment and rollback.

Build model registries, feature stores, experiment tracking and monitoring systems.

Monitor model drift, data quality, latency, resource usage and service reliability.

Standardise deployment templates and operational playbooks for AI teams.

Hard-skills

Strong knowledge of Kubernetes, Docker, cloud services, CI/CD and observability tools.
Experience with MLflow, Kubeflow, Airflow, Feast or similar MLOps tools.

Education & Training

Bachelor’s degree or above in Computer Science, Software Engineering or related fields.
Background in DevOps, platform engineering or ML engineering is preferred.

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