AI Glossary · AI Roles & Careers
MLOps / LLMOps Engineer
Owns the deployment, monitoring, evaluation and cost control of production ML/LLM systems — CI/CD, observability, rollback, drift detection.
Definition
What is MLOps / LLMOps Engineer?
MLOps / LLMOps Engineer is owns the deployment, monitoring, evaluation and cost control of production ML/LLM systems — CI/CD, observability, rollback, drift detection.
- Category
- AI Roles & Careers
- Glossary set
- 9 related terms
- Audience
- Enterprise AI leaders
Why does MLOps / LLMOps Engineer matter in enterprise AI?
MLOps / LLMOps Engineer matters in enterprise AI programs because it helps business and technology leaders align vocabulary, scope, ownership, and measurable outcomes.
Related terms in AI Roles & Careers
- AI Product Manager
- Owns discovery, scoping and lifecycle of AI features — writing evals as acceptance criteria, managing model/prompt versions, and quantifying business impact.
- AI Solutions Architect
- Designs end-to-end AI systems: models, retrieval, tools, integration, security, cost. The senior engineering role that bridges enterprise architecture and applied AI.
- Chief AI Officer (CAIO)
- Executive accountable for enterprise-wide AI strategy, portfolio, governance and value realization. Increasingly a peer to CIO, CDO and CISO.
- Conversation Designer
- Designs bot and voice-AI dialogues — intents, prompts, tone, fallback logic, containment. Blends UX writing, linguistics and product design.
- Data Scientist
- Applies statistics and ML to business problems; owns modeling, experimentation and analysis. Distinct from AI engineer, though the roles converge on LLM tooling.
- Model Risk Officer
- Governance role (rooted in banking SR 11-7) responsible for validating, documenting and monitoring model risk — extended in most enterprises to cover LLMs and agents.