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AI Engineer (also: Generative AI Engineer, LLM Engineer, Applied AI Engineer)SOC 15

AI Engineer (also: Generative AI Engineer, LLM Engineer, Applied AI Engineer) Resume Example — ATS-Optimized for 2026

Free ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume example with the exact ATS keywords, section order, and bullet patterns that score 96+ on real applicant tracking systems in 2026.

Quick answer: A ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume should lead with Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) in the professional summary, follow the ATS-safe section order, and target 96+ on applicant tracking systems. Keep it single-column with quantified bullets and avoid two.

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Projected ATS score
96/100
Works for
Entry (0–2 yrs) • Mid (3–6 yrs) • Senior (7+ yrs) • Staff/Principal
AI Engineer (also: Generative AI Engineer, LLM Engineer, Applied AI Engineer) resume template preview

Built on the JobVouch data science layout — single column, ATS-safe.

What hiring teams look for

AI Engineer (also: Generative AI Engineer, LLM Engineer, Applied AI Engineer)s win ATS screens by front-loading the highest-frequency job-description keywords (Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, Prompt Engineering) in the summary and skills blocks, then proving each in quantified bullets that show scope, method, and business outcome.

Top ATS keywords for a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume

Frequencies reflect how often each keyword appears in current ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) job descriptions. Higher-frequency terms belong in your summary and Skills block.

KeywordJD frequencyWhere to place it
Python61% of software/AI JDs (ResumeAdapter 2026); confirmed TealHQ top hard skillSummary + Skills + top Experience bullets
Large Language Models (LLMs)Fastest-growing AI keyword 2026 (ResumeAdapter); confirmed resume.ioSummary (use both "LLMs" and "Large Language Models") + Skills
Retrieval-Augmented Generation (RAG)Baseline expectation for any LLM role 2025–2026 (InterviewQuery, ResumeAdapter)Summary + first Experience bullet for LLM roles
LangChainModal AI Engineer skill per Dice 2026 data (ResumeAdapter, InterviewQuery)Skills section + Experience bullets
Prompt EngineeringLinkedIn Skills on the Rise 2026 top ranking (ResumeAdapter, MentorCruise)Summary + Skills
PyTorchCore framework; confirmed across resume.io, ResumeWorded, TealHQSkills section (ML/DL category)
Vector DatabasesNot having it is "a red flag in 2026" (ResumeAdapter, InterviewQuery)Skills + Projects section
Machine Learning PipelinesTop hard skill per TealHQ analysis of 1,000 AI JDsExperience bullets
Natural Language Processing (NLP)Confirmed resume.io, ResumeWorded, MentorCruise; use both "NLP" and full formSummary + Skills
Docker / KubernetesCore deployment stack; confirmed resume.io, ResumeWorded, TealHQSkills (MLOps category)
AWS / Azure / GCP73% of backend/AI JDs mention cloud (ResumeAdapter 2026)Skills (Cloud category) + Experience
MLflowMLOps tracking; confirmed InterviewQuery, ResumeAdapterSkills (MLOps category)
Fine-TuningConfirmed resume.io, ResumeAdapter, TealHQSkills + Projects if applicable
FastAPIAI service deployment; confirmed ResumeAdapter, TealHQSkills + Experience
TensorFlowCore framework; confirmed resume.io, ResumeWordedSkills section (ML/DL category)
API IntegrationApplication-layer expectation; confirmed MentorCruise, InterviewQueryExperience bullets
CI/CDDeployment pipeline; confirmed resume.io, MentorCruiseSkills (MLOps)
SQLNon-negotiable across data/AI roles (ResumeAdapter 2026)Skills (Data category)
EmbeddingsCore GenAI concept; confirmed ResumeAdapter, InterviewQuerySummary + Skills
LlamaIndexOrchestration framework; confirmed ResumeAdapter, InterviewQuerySkills (LLM Stack)

Section order that scores

The order matters. ATS parsers weight content closer to the top, so leading with the right sections lifts your keyword score before the parser ever reaches your work history.

  1. 1

    Contact Block (name, email, phone, LinkedIn URL, GitHub URL, portfolio/personal site if applicable

    no icons, no photo, no mailing address)

  2. 2

    Professional Summary (2

    3 sentences: seniority + primary stack + one flagship metric)

  3. 3

    Technical Skills (grouped by category

    see Layout Spec)

  4. 4

    Work Experience (reverse-chronological; 3

    6 bullets per role)

  5. 5

    Projects (1

    3 shipped AI projects with measurable outcomes; include model/framework/metric)

  6. 6

    Education (degree, institution, graduati

    Education (degree, institution, graduation year; GPA only if 3.7+ and within 3 years)

  7. 7

    Certifications (dedicated section; list

    Certifications (dedicated section; list name, issuing org, year)

Bullet examples that work

Each follows the STAR-with-stack pattern: action verb, tool or method, business outcome, and a hard number.

Architected a RAG pipeline using LangChain and Pinecone on AWS Bedrock, reducing hallucination rate by 34% and cutting average response latency from 2.1s to 0.7s (p95)

Fine-tuned Llama-3-8B using QLoRA on 45K proprietary support tickets, achieving 91% intent classification accuracy vs. 74% baseline — deployed via vLLM serving 12K requests/day

Built multi-agent orchestration system with LangGraph and GPT-4o to automate contract review workflow, reducing attorney review time by 60% across 800 documents/month

Productionized ML inference pipeline (PyTorch + TorchServe + Kubernetes) for real-time fraud scoring at 8K QPS with p99 latency under 45ms

Integrated OpenAI Assistants API with FastAPI backend and pgvector knowledge base, enabling semantic search over 2M product SKUs with 92% user satisfaction score

ATS killers to avoid

Each of these is documented to break parsing across major ATS platforms. Avoid them and your score climbs even without rewriting a single bullet.

  • Two-column or sidebar layout — ATS concatenates columns into nonsense text, destroying all keyword extraction
  • Proficiency bars or star ratings in the Skills section — visual elements are invisible to parsers; ATS reads zero keywords from them
  • Icons replacing text in contact block (phone icon, email icon, LinkedIn bird icon) — many parsers render as [NULL] or skip entirely
  • Tables used to organize skills or experience — same concatenation problem as two-column layouts
  • Contact information placed in document header/footer — ATS scrapers frequently skip header/footer regions
  • Custom embedded fonts or Canva-exported PDFs — layout is image-encoded; parsers see zero text Profession-specific content killers:
  • Generic "AI expert" or "machine learning professional" without naming the specific stack — modern ATS uses semantic context, not just keyword presence; vague claims score near zero
  • Listing "ChatGPT" or "AI tools" without framework specificity — recruiters filter these out as noise; name the API, the framework, the model version
  • Mixing AI Engineer keywords with ML Research keywords indiscriminately — ATS role-matching treats "CUDA kernel optimization" and "LLM orchestration" as different role clusters; mismatches lower overall relevance score
  • Omitting vector database experience entirely — confirmed red flag for any 2026 LLM role (ResumeAdapter, InterviewQuery)

Frequently asked questions

What ATS score should a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume target?

Aim for 96 or higher. The structure on this page combines a single-column layout, the section order recommended for ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) roles, and 15-25 validated keywords placed in the summary and top bullets so the resume earns location-weighted points where ATS parsers look first.

How long should a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume be?

One page for 0-5 years of experience and two pages for 6+ years. Never truncate quantified achievements to fit a single page — let the document flow cleanly to page 2 rather than dropping metrics that prove impact.

What are the most important keywords on a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume?

The highest-frequency keywords for ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) job descriptions are Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, Prompt Engineering. Place the top three in your summary (1.5x ATS weight) and repeat each in the top bullet of the role where you used it.

Where should skills go on a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume?

see Layout Spec) Group skills with inline category labels rather than rendering them in tables or visual grids — ATS parsers drop or scramble table cell contents.

What's the biggest formatting mistake on ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resumes?

Two-column or sidebar layout — ATS concatenates columns into nonsense text, destroying all keyword extraction Single-column layouts with plain text section headers parse reliably across every major ATS, while creative templates with sidebars, icons, or skill bars routinely lose data during parsing.

Should I include a photo or objective on a ai engineer (also: generative ai engineer, llm engineer, applied ai engineer) resume?

No photo on US resumes — most ATS platforms either reject embedded images or strip them, and some companies discard photo resumes for compliance reasons. Replace any objective statement with a 3-4 sentence professional summary that includes your top keywords.

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