How to write a llmops engineer resume
A strong LLMOps engineer resume shows you run LLM systems in production the way SREs run services: serving infrastructure (vLLM, GPU autoscaling), eval pipelines gating deploys, tracing/observability (per-request token costs, latency percentiles), and guardrails — each with numbers (e.g. "Built the LLM gateway serving 2M requests/day: p95 1.1s, per-team cost attribution, and eval-gated prompt deploys"). It is the newest of the ops titles, so translate MLOps, DevOps, or platform experience explicitly into LLM terms.
What recruiters and ATS look for in a llmops engineer resume
LLMOps barely existed as a title before 2024, so almost everyone applying is translating adjacent experience — the resume that wins makes the translation concrete instead of hoping the recruiter does it. The stack recruiters search: vLLM or serving frameworks, LLM gateways, observability tools (Langfuse/LangSmith-style tracing), eval pipelines, prompt/version management, token-cost optimization, guardrails. What distinguishes LLMOps from classic MLOps is the operational surface: nondeterministic outputs, prompt changes as deploys, per-token economics, and safety filtering — show you have managed at least two of those in production and the title fits.
Section order: Summary → Experience → Skills (grouped: Serving / Observability / Evals / Infra) → Projects → Education.
ATS keywords for a llmops engineer resume
These are the keywords most llmops engineer job descriptions use as ATS-filter inputs. Include the ones you genuinely have evidence for in your Skills section.
Starter Skills section
A starting point for your Skills section. Prune to what you genuinely have evidence for.
Best action verbs for llmops engineer bullets
Lead every bullet with a strong, specific verb. For this role, the strongest openers are:
Example bullet points (before → after)
Three rewrites following the action-verb / quantified-outcome pattern. Replace the specifics with your own. Never invent numbers.
LLMOps Engineer resume FAQ
LLMOps is the operations discipline for LLM-powered systems: serving and scaling models, tracing calls, gating prompt and model changes with evals, and controlling token costs. It differs from classic MLOps because the artifacts changing in production are prompts and model choices, outputs are nondeterministic, and the economics are per-token — the resume should show you have handled those specifics.
Serving (vLLM or managed equivalents), an observability/tracing setup for LLM calls, eval pipelines wired into CI, prompt/version management, cost optimization (caching, routing, compression), and guardrails. Under it all: Kubernetes, Python, and GPU-aware infrastructure skills.
Take one LLM workload end-to-end — even internal: stand up serving or a gateway, add tracing and an eval gate, publish the cost/latency numbers. One concrete story ('built the gateway, cut spend 40%, p95 under 1.5s') converts an ops resume into an LLMOps resume, because almost no candidate has years of it.
Related guides: How to write a mlops engineer resume · How to write a ai infrastructure engineer resume · How to write a inference engineer resume · How to write a software engineer resume · How to write a devops engineer resume
Build it free, score it instantly
Free forever for one resume, no expiry, no credit card. Or check your current resume against 60+ ATS checks, no sign-up needed.