LLMOps Engineer
Definition
An LLMOps Engineer specializes in the operational aspects of LLM applications: deployment, monitoring, evaluation, cost optimization, and maintaining reliability of AI systems in production.
Why It Matters
As AI applications move from demos to production, operational excellence becomes critical. LLMOps Engineers ensure that AI systems are reliable, cost-effective, and continuously improving. They bridge the gap between building AI features and running them sustainably at scale.
Key Responsibilities
- Deployment: Setting up inference infrastructure, managing model serving, handling scaling
- Monitoring: Tracking latency, costs, error rates, and output quality in real-time
- Evaluation: Building automated testing pipelines, regression detection, quality benchmarks
- Cost Optimization: Managing token usage, implementing caching, optimizing prompt efficiency
- Observability: Implementing tracing and logging with tools like LangSmith, Langfuse, or Helicone
Career Path
LLMOps roles often evolve from: DevOps engineers adding AI expertise, ML Engineers moving toward operations, or AI Engineers specializing in production systems. The role requires both software engineering skills and understanding of LLM-specific challenges like prompt drift, hallucination monitoring, and evaluation metrics.