LLM Engineer / LLMOps. The role, the market signal, and how to build it in your org.
Builds, adapts and operates applications and services around large language models. Work can include retrieval, fine-tuning, evaluation, serving and monitoring; responsibilities overlap with other engineering roles.
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.
63 postings · 15 distinct titles · from 264,613 real job postings · see the source snapshot →
What the postings ask this role to do
934 tasks extracted from real LLM Engineer / LLMOps job descriptions, classified Automate / Augment / Human-only. In this source classification, 3.6% of task statements are automation candidates. The share does not measure working time or demonstrated agent performance.
- Automate processes
- Monitor llm performance.
- Fine-tune large language models with emphasis on instruction fine-tuning and domain adaptation.
- Enhance model relevance and performance in specific contexts
- Analyze model outputs.
- Iterate on training processes.
- Deploy enterprise-grade solutions using generative and agentic ai frameworks.
- Make team decisions.
- Collaborate and manage the team to perform.
- Mentor junior team members to enhance their skills and knowledge in model development.
- Engage with multiple teams and contribute on key decisions.
- Provide solutions to problems for immediate team and across multiple teams.
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.