15. MLOps Engineer

Evidence label: Established role family, with substantial overlap with AI Platform Engineer in some organizations.

What they do

Bridge the gap between model development and production infrastructure. While ML engineers build and fine-tune models and AI engineers build applications, MLOps engineers own the operational layer: CI/CD pipelines for models, deployment infrastructure, monitoring for drift and degradation, and the systems that keep models reliable in production over time.

Responsibilities

• Build and maintain CI/CD pipelines for model training, evaluation, and deployment
• Monitor production models for performance drift, data drift, and prediction quality degradation
• Design feature stores and model registries
• Implement A/B testing and canary deployments for new model versions
• Manage compute infrastructure for training and inference (cost optimization)
• Build observability tooling: metrics, logging, alerting for model behavior in production
• Establish model versioning and rollback procedures

Required skills

Technical	Soft
Python (fluent)	Infrastructure mindset
Cloud ML platforms (SageMaker, Vertex AI, Azure ML)	Cross-team collaboration (ML + Infra)
Kubernetes, Docker, infrastructure as code	Reliability engineering instinct
MLflow, Weights & Biases, or similar experiment tracking	Incident response discipline
Data pipeline tools (Airflow, Prefect, dbt)
Monitoring and observability (Prometheus, Grafana)

The distinction that matters

ML engineers ask: "Does the model work?" MLOps engineers ask: "Does the model keep working?" The operational lifecycle of a model (monitoring, retraining triggers, rollback procedures, cost per inference) is entirely separate from building it. Companies that skip this role discover it when a model silently degrades in production and nobody notices until user complaints spike.

Entry paths

DevOps/platform engineer adding ML knowledge, ML engineer who gravitates toward infrastructure, data engineer moving toward model operations.