Private Draft

The 29 personas behind AI

We’ve organized every stage and persona in the AI supply chain, informed by real recruiting at frontier companies. Click any row to see matching profiles from our talent graph.

Shaped by Industry Experts

← ATOMS & ENERGYUSERS & MARKETS →

← Back

Model Operations

Keeps models running in prod

Model Operations

Summary

Known as: ML Engineer, MLOps Engineer, Software Engineer (ML), Infrastructure Engineer

Production reliability for deployed models: deployment pipelines, release management, monitoring, drift detection, incident response, and model lifecycle management. For LLM applications, includes prompt orchestration and behavior monitoring. For agent systems, includes trajectory collection, environment-based testing, and optimization loop management.

Specializations

Deployment & Release — CI/CD for models, canary rollout, rollback, and release gating. Owns the pipeline from validated checkpoint to production endpoint.

Monitoring & Drift — Production telemetry, data and behavior drift detection, alerting, and quality dashboards. Detects when model behavior degrades before users do.

Incident Response & Lifecycle — Postmortems, model retirement, version management, and the on-call response when production models misbehave. Owns the full lifecycle from deployment through deprecation.

Where the Work Lives

[1]Substrate

[2]Compute

[3]Intelligence

Secondary

Manages model lifecycle — rollouts, regressions, version management across the model development loop.

[4]Systems

Primary

Production reliability: deployment pipelines, monitoring, drift detection, and incident response for live models.

[5]Distribution

Candidate Archetypes