LLMOps & AI Infrastructure Engineer Resume Examples & Writing Guide
Show the tokens per second, the GPU utilisation and the on-call record behind every model you kept running
By CraftMyDocs Editorial · Updated October 5, 2026 · How these guides are produced
Professional Summary Example
"LLMOps engineer with 5 years in infrastructure and 2 years running production LLM platforms for a product used by 1.2M people. Own the serving layer for six models on a 64-GPU Kubernetes cluster: raised GPU utilisation from 38% to 71% with continuous batching in vLLM, held p95 time-to-first-token under 600ms and cut inference spend by $410K a year. Built the prompt and model release pipeline, with offline evals and canary traffic as automated gates, so teams ship weekly with no incidents attributed to prompt changes. Looking for an AI infrastructure team operating at scale."
Typical US Salaries Across Technology Roles
Industry-wide range, not a LLMOps Engineer-specific figure. Use it to sense-check an offer, then look up this exact title on the BLS Occupational Outlook Handbook or a salary survey for your region.
- Basis
- Industry-wide estimate for Technology roles
- Region · currency
- United States · USD, annual base pay
- Source
- Estimated US national base-pay ranges, reviewed 2026-08. Actual pay varies by location, employer and specialization.
Top Skills to Put on Your Resume
Recruiters and ATS scanners look for these exact skills on LLMOps Engineer resumes:
Best Action Verbs for LLMOps Engineer
Open each LLMOps Engineer bullet with one of these verbs — they match how Technology postings describe the work:
LLMOps Engineer Experience Bullet Point Repository
Select a category and click Copy Bullet to paste directly into your resume.
Migrated six models from a custom Flask service to vLLM with continuous batching and paged attention; throughput per GPU rose 2.6x and p95 time-to-first-token fell to 580ms.
Quantised a 70B model to INT8 and validated quality on a 600-case eval, saving 16 GPUs without a measurable drop in task success.
Introduced GPU bin-packing and queue-based scheduling on Kubernetes, raising utilisation from 38% to 71% and saving $410K annually.
Built per-team token and cost attribution dashboards, which let product owners remove two features that spent more than they earned.
Created a release pipeline in which every prompt or model change runs offline evals and a 5% canary before rollout.
Instrumented traces across retrieval, tool calls and generation with OpenTelemetry; mean time to isolate a bad answer fell from hours to 15 minutes.
Led post-incident reviews for three provider outages and added fallback routing between two vendors.
Built the prompt and model release pipeline, with offline evals and canary traffic as automated gates, so teams ship weekly with no incidents attributed to prompt changes.
Weak vs. Strong Bullet Example
"Responsible for llm serving & inference optimisation (vllm, tensorrt-llm) and other tasks as assigned."
"Migrated six models from a custom Flask service to vLLM with continuous batching and paged attention; throughput per GPU rose 2.6x and p95 time-to-first-token fell to 580ms."
Top Resume Mistakes for LLMOps Engineer Applicants
"Skilled in llm serving & inference optimisation (vllm, tensorrt-llm)" is a claim any applicant can make. Show it with a result instead: "Migrated six models from a custom Flask service to vLLM with continuous batching and paged attention; throughput per GPU rose 2.6x and p95 time-to-first-token fell to 580ms."
Applicant tracking systems match wording literally. If the posting says "LLM Serving & Inference Optimisation (vLLM, TensorRT-LLM)", "GPU Cluster Scheduling & Capacity Planning", "Kubernetes, KServe & Ray Serve", use those exact phrases — not a synonym you prefer.
An ATS reads text, not graphics — a five-dot bar next to "Kubernetes, KServe & Ray Serve" is invisible to it. Write each one as plain text in a Skills line, and name it again in the LLMOps Engineer bullet where you used it.
LLMOps Engineer ATS Optimization Checklist
- Headline: Your title line reads "LLMOps Engineer" (or the posting's exact title), not a creative variant.
- Keywords present: LLM Serving & Inference Optimisation (vLLM, TensorRT-LLM), GPU Cluster Scheduling & Capacity Planning, Kubernetes, KServe & Ray Serve, Evaluation Pipelines & Release Gating, LLM Observability (Langfuse, OpenTelemetry, Arize Phoenix) — each one appears at least once in Skills or Experience.
- Verbs first: Bullets open with LLMOps Engineer verbs such as Provisioned, Scaled, Instrumented, Benchmarked.
- File Format: Send a PDF (or DOCX if the posting asks) without password protection, named like
FirstName-LastName-LLMOps-Engineer-Resume.pdf. - Standard Headings: Use "Work Experience", "Education" and "Skills" — and split Skills into Tools (Kubernetes, KServe & Ray Serve, LLM Observability (Langfuse, OpenTelemetry, Arize Phoenix), Infrastructure as Code (Terraform, Helm)); Technical (LLM Serving & Inference Optimisation (vLLM, TensorRT-LLM), GPU Cluster Scheduling & Capacity Planning, Evaluation Pipelines & Release Gating); Professional (Incident Response & Reliability Culture).
- Font & Margins: Use 10-12pt standard fonts (Inter, Arial, Roboto) with 0.5 to 1 inch margins.
Complete LLMOps Engineer Career & Writing Guide
Somebody has to keep the language model running at 3 a.m. Running a large language model in production is a different discipline from training one: the unit of cost is the token, the unit of failure is a silently worse answer rather than a stack trace, and the hardware is scarce, expensive and easy to leave idle. LLMOps and AI Infrastructure Engineers own that discipline. They build the serving layer, schedule GPUs, put evaluations in the release path, trace requests across retrieval and tool calls, and make sure finance can see what each product feature costs to run.
Because the field is new, hiring managers read these resumes for specificity. 'Experience with MLOps and cloud' blends into every other application. 'Raised GPU utilisation from 38% to 71% using continuous batching' does not. The sections below help you turn your infrastructure work into those kinds of statements, explain how to separate LLMOps from classic MLOps, and show how to organise a skills section that works for both platform teams and AI product teams.
1. How to Write a Professional Summary
Treat the summary as an operations report in miniature: the system, the scale, the improvement and the reliability record.
- System and scale. 'Own the serving layer for six models on a 64-GPU Kubernetes cluster serving 1.2M users.' Scale lets a reader judge seniority within a sentence.
- Efficiency win. One measured improvement with technique, for example batching, quantisation, autoscaling or caching.
- Reliability or governance win. An SLO met, an incident rate lowered, or a release gate that stopped bad changes.
- Direction. Platform, product-facing or research infrastructure.
Avoid listing 'LLMOps, MLOps, DevOps, AIOps' as if they were four skills; the overlap looks like keyword stuffing. Choose the label that fits the posting and describe the work. If you are coming from SRE or platform engineering, say that plainly and then show the LLM-specific work: GPU scheduling, model serving, evaluation gates. If you are coming from ML engineering, lead with the production and reliability bullet, since infrastructure teams hire for operational maturity first.
2. Highlighting Your Work Experience
LLM infrastructure bullets should reference a constraint (latency, cost, capacity or risk), the technique you applied and the measured result. Group them under the layer of the stack you worked on.
Serving and performance
- “Migrated six models from a custom Flask service to vLLM with continuous batching and paged attention; throughput per GPU rose 2.6x and p95 time-to-first-token fell to 580ms.”
- “Quantised a 70B model to INT8 and validated quality on a 600-case eval, saving 16 GPUs without a measurable drop in task success.”
Capacity and cost
- “Introduced GPU bin-packing and queue-based scheduling on Kubernetes, raising utilisation from 38% to 71% and saving $410K annually.”
- “Built per-team token and cost attribution dashboards, which let product owners remove two features that spent more than they earned.”
Release and quality
- “Created a release pipeline in which every prompt or model change runs offline evals and a 5% canary before rollout.”
Observability and incidents
- “Instrumented traces across retrieval, tool calls and generation with OpenTelemetry; mean time to isolate a bad answer fell from hours to 15 minutes.”
- “Led post-incident reviews for three provider outages and added fallback routing between two vendors.”
Show on-call and ownership honestly: 'primary on-call for the inference platform, 99.95% availability over 12 months'.
3. Selecting the Right Skills
Order your skills block by layer so a reader can find the part of the stack they care about.
- Serving and optimisation: vLLM, TensorRT-LLM, Triton Inference Server, TGI, quantisation (INT8, FP8, AWQ), speculative decoding, KV-cache management, batching strategies.
- Orchestration and compute: Kubernetes, KServe, Ray Serve, Karpenter, Kueue or Slurm, NVIDIA GPU Operator and DCGM metrics, spot and reserved capacity strategies.
- Pipelines and release: MLflow, Weights & Biases, GitHub Actions or Argo, prompt and model registries, evaluation-as-CI, canary and shadow deployments.
- Observability: OpenTelemetry, Prometheus and Grafana, Langfuse, Arize Phoenix or LangSmith, structured logging, SLO design.
- Cloud and IaC: AWS, GCP or Azure GPU instances, Terraform, Helm, networking and private endpoints.
- Gateways and safety: LLM gateways, rate limiting, caching, fallbacks, guardrails, PII handling.
Add Python and one systems language such as Go or Rust if you have genuinely used them for tooling. Treat soft skills as evidence: 'ran blameless post-incident reviews' and 'partnered with finance on unit economics' communicate more than 'teamwork'. Keep experimental tools in a short secondary line so your primary list reflects what you operate in production.
4. Education, Licenses & Certifications
LLMOps and AI infrastructure roles draw from computer science, electrical or computer engineering, and sometimes physics or applied mathematics. A bachelor's degree is the usual requirement; a master's is helpful for roles close to research infrastructure but rarely mandatory when you have operated real systems.
Present each degree with institution, field and year. Include coursework or a thesis only when it relates to the work: distributed systems, operating systems, high-performance computing, computer architecture or parallel programming.
Certifications and training with real value in this niche:
- Kubernetes: CKA or CKAD from the CNCF.
- Cloud: AWS, Azure or Google Cloud associate or professional-level certifications, particularly in architecture or DevOps.
- GPU and performance: NVIDIA Deep Learning Institute courses on multi-GPU training or inference optimisation.
- Reliability: an SRE or observability course from a recognised provider.
Open-source contributions can substitute for formal credentials. A merged patch to vLLM, KServe, Ray or an observability library, or a benchmark write-up comparing serving engines on specific hardware, shows depth that a certificate cannot. List them under a short 'Open source and writing' heading with plain URLs.
5. Layout & ATS Formatting Rules
An infrastructure resume should look as orderly as the systems it describes.
- Length: one page for under eight years of experience; two pages for staff-level candidates who have designed platforms across teams.
- Order: Summary, Skills, Experience, Open source and writing, Education, Certifications.
- Context line: start each role with a one-line description of the environment: cluster size, number of models, requests per day, team size.
- Bullets: four to six per role. Pair every performance claim with its baseline and the technique used.
- Tables and diagrams: avoid them. They break in ATS parsing and rarely add clarity in a resume.
- Terminology: write terms the way job postings do, for example 'LLM serving', 'inference optimisation', 'GPU scheduling' and 'observability'. Spell out an acronym once if it is uncommon.
- Links: GitHub, benchmark write-ups or conference talks as plain URLs.
- File: text-based PDF, 10 to 11 point sans-serif font, standard margins.
Check every number against your records. Infrastructure interviewers will ask how you measured utilisation, which workload you used and what you traded away to achieve the saving.
Frequently Asked Questions
What is the difference between MLOps and LLMOps on a resume?
MLOps covers the lifecycle of conventional models: feature pipelines, training, registry, deployment and drift monitoring. LLMOps adds concerns specific to large language models: prompt and context versioning, evaluation of open-ended outputs, token-level cost control, GPU-heavy serving, retrieval systems, guardrails and tracing across multi-step agent calls. Show both if you have them, but give LLMOps bullets their own weight: serving throughput, time-to-first-token, eval gates and cost per request tell a reader you have worked with LLM-specific constraints.
Which infrastructure metrics should I put on an AI Infrastructure Engineer resume?
Choose a few that connect to cost and reliability: GPU utilisation, tokens per second per GPU, p95 time-to-first-token and end-to-end latency, cost per million tokens, availability against an SLO, autoscaling reaction time and the number of model versions rolled out per month. Add the scale: GPUs in the cluster, requests per second, or models served. A before and after on any one of these, with the technique you used, is more persuasive than a list of tools.
I run MLOps on classical models. How do I move into LLMOps?
Build evidence of LLM-specific work, even outside your job: self-host an open model with vLLM, add tracing and an evaluation suite, load-test it and publish the numbers and costs. In your resume, state the transferable foundation (CI/CD, Kubernetes, monitoring, feature stores) and add one or two LLM-specific bullets from work or a documented project. A summary line such as 'MLOps engineer expanding into LLM serving and evaluation' signals your direction honestly and helps keyword matching.
Do I need to have worked with GPUs to apply for an LLMOps role?
Direct GPU operations experience is a strong advantage for roles that run their own models, but it is not required for teams that call hosted APIs. In that case your resume should emphasise gateways, caching, rate limiting, observability, cost attribution and evaluation. If you apply to self-hosting teams without GPU experience, show adjacent skills: Kubernetes scheduling, resource quotas, performance profiling and capacity planning, plus a personal serving project with benchmark results.