As organizations shift from AI experimentation to full-scale production, MLOps Engineers are essential. They design the infrastructure to automate the deploymen
An MLOps Engineer builds the infrastructure that keeps AI systems running in production: deployment pipelines, model serving, monitoring, and the feedback loops that catch quality drift before users do. If AI Engineers build the application, MLOps Engineers build the factory around it: CI/CD for models and prompts, GPU-efficient serving, observability, and rollback paths.
In 2026 the role has expanded into LLMOps: managing prompt and model versioning, eval-gated releases, token cost optimization, and inference routing across multiple providers. As companies move from one AI pilot to dozens of production AI features, the MLOps Engineer is the person who makes that scale survivable.
They build and run the infrastructure behind AI features: deployment pipelines, model serving, monitoring dashboards, eval gates on releases, and cost controls. A typical week mixes Kubernetes and CI/CD work with LLM-specific tasks like prompt versioning and drift investigation.
LLMOps applies MLOps discipline to LLM-based systems: instead of retraining models, you version prompts and contexts, run evaluation suites before releases, trace agent behavior, and manage token costs across providers. Most 2026 MLOps roles include both.
Roughly $140k–$210k base in the US, with senior platform engineers at AI-heavy companies exceeding that once equity is included. Inference cost-optimization expertise commands the largest premium.
Yes: it's the most common path. Your Kubernetes, CI/CD, and observability skills transfer directly; add model serving (vLLM), eval pipelines, and token cost management to make the jump.