MLOps Lead. The role, the market signal, and how to build it in your org.
Leads the operation of machine-learning systems, including deployment pipelines, monitoring, retraining and rollback. Agrees release and recovery procedures with engineering and operating owners.
In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.
Source snapshot 2026-07-17. Postings do not confirm that a vacancy is still open.
48 postings · 43 distinct titles · from 264,613 real job postings · see the source snapshot →
What the postings ask this role to do
867 tasks extracted from real MLOps Lead job descriptions, classified Automate / Augment / Human-only. In this source classification, 3.1% of task statements are automation candidates. The share does not measure working time or demonstrated agent performance.
- Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.
- Participate in code reviews.
- Ensure ml workflows are auditable.
- Ensure ml workflows are traceable.
- Develop observability for ml systems.
- Ensure ml workflows are reproducible.
- Mentor team members.
- Collaborate with data scientists and engineers to integrate ml models into production workflows.
From the market's version of this role to your version of it
Start with the work, not the org chart.
Define what this role will build, configure, decide and check. Connect that work to an enterprise-specific project and agreed assessment evidence.