AI Engineer resume (CV): examples, bullets and keywords
An AI engineer resume has to prove you have shipped LLM systems that real users rely on, not just built demos. Put one production RAG app or agent near the top, say how you measured its quality (evals), its latency and its cost, and keep it to one page.
What the recruiter checks first
For AI Engineer roles, these decide whether you get a call.
- 01One LLM feature in production with real users, and a number: accuracy on an eval set, adoption, latency or cost per task.
- 02Evals: how you built the test set, what you measured (faithfulness, hallucination rate, task success) and the regressions it caught.
- 03Production engineering: Python or TypeScript, APIs, async code and streaming, monitoring, retries and fallbacks.
- 04Judgement on trade-offs: which model and why, RAG versus fine-tuning, and when an agent is the wrong tool.
Recruiters spend about 7.4 seconds on the first skim, and simple one-column layouts with clear headings held attention best (Ladders eye-tracking study, 2018).
Format and section order
One page under about 8 years of experience. Two pages only for staff-level engineers or research-heavy profiles.
Section order
- 1Name and contact (city, email, LinkedIn, GitHub, live demo link)
- 2Two-line summary with your best production AI result
- 3Experience (most recent first, 3–5 bullets each)
- 4Projects with evals (essential for freshers and switchers)
- 5Skills (grouped: languages, LLM stack, evals and observability, infrastructure)
- 6Education, publications and certifications
In India “CV” and “resume” mean the same one- or two-page document. Skip the photo, date of birth and marital status. Do not put current CTC on the CV; recruiters ask for it, and for your notice period, in the application form. Indian AI engineer posts, such as Workfall’s in Bengaluru, name specific tools (LangChain, LlamaIndex, FastAPI, vector databases), so name the ones you used inside your bullets.
US recruiters expect one page, no photo and no personal details. Lead every bullet with a verb and a number. Link a live demo or a repo with an eval table, so a screener can check your work in a minute.
Profile summary examples
Two lines at the top. Lead with your strongest result for this role.
Backend engineer with 2 years in Python and FastAPI. Built and deployed a RAG assistant over our 3,000-page internal wiki; 120 colleagues use it weekly, and it scores 86% on a 150-question eval set. Moving into AI engineering.
AI engineer with 4 years of software experience, the last 2 shipping LLM features in B2B SaaS. Built the RAG and agent stack behind our support copilot, with evals that gate every prompt and model change. Cut p95 latency from 7.2s to 2.5s and cost per task by 45%.
Lead AI engineer. Owns the LLM platform for [N] product teams: model gateway, eval harness and production monitoring. Took [N] AI features from prototype to production and cut model spend by [X]% with routing and caching.
AI Engineer resume bullet bank
Swap the [brackets] for your own numbers. Each bullet: verb, what you did, measurable result.
Shipping LLM features
- Shipped [RAG assistant or agent] from prototype to production in [N] weeks; [N] users, [X]% weekly active.
- Built retrieval over [N] documents with hybrid search and reranking; retrieval hit rate rose from [X]% to [Y]% on [N] eval questions.
- Built a tool-calling agent for [workflow] with [N] tools and human approval above [threshold]; [X]% of cases resolved without a human.
Evals and quality
- Built a [N]-example gold dataset from real [tickets or queries]; it gates every prompt and model change and caught [N] regressions.
- Cut unsupported answers from [X]% to [Y]% with citations, a refusal path and a faithfulness check in the eval suite.
- Set up an LLM-as-judge grader with a written rubric; [X]% agreement with human labels on [N] samples.
Latency, cost and reliability
- Cut p95 latency from [X]s to [Y]s with streaming, parallel tool calls and a smaller model for first drafts.
- Reduced cost per task by [X]% with prompt caching and routing easy requests to a cheaper model, with no drop in eval score.
- Added retries with backoff, a provider fallback and alerts; failed requests fell from [X]% to [Y]%.
Production and safety
- Added prompt-injection defences and kept [N] attack examples in the eval suite; zero data-leak incidents in [N] months.
- Built tracing and quality dashboards for [N] LLM endpoints; time to detect a quality regression fell from [N] days to [N] hours.
- Migrated [feature] from [model A] to [model B] with a full eval run and a flag-gated rollout; [X]% lower cost at the same score.
Before and after
The same experience, rewritten so a recruiter sees the result.
Built a chatbot using LangChain, OpenAI and Pinecone.
Built a support assistant with hybrid retrieval over 2,400 help articles; 84% correct on 300 real tickets, now answering 1,100 questions a day.
Why it works: The stack is not the result. Say what it does, how you measured it and who uses it.
Worked on improving LLM accuracy and reducing hallucinations.
Built a 300-example eval set with support leads and added a citation check; unsupported answers fell from 14% to 3% before launch.
Why it works: Shows how quality was measured, with a baseline, not just that it improved.
Optimised performance of AI features.
Cut p95 latency from 8s to 2.4s by streaming responses and running retrieval and tool calls in parallel; chat drop-off fell by a third.
Why it works: “Optimised” says nothing. Name the metric, the lever and what users felt.
AI Engineer resume keywords
Taken from real job posts. Use the ones you can prove, inside bullets, not as a list.
Paste your resume text. It stays in your browser; nothing is uploaded.
No experience yet? Build proof
Each of these can become a line on your resume and a story in the interview.
RAG app with a public eval table
Pick documents people actually search (your university’s rules, a government scheme FAQ, an open-source project’s docs). Build retrieval with citations, write 50–100 real questions with expected answers, and publish accuracy, latency and cost per query in the README.
Proves: You measure quality instead of eyeballing demos, which is the first thing AI engineer screeners check.
A small agent with guardrails
Build an agent with two or three tools for one real job, such as triaging GitHub issues. Cap the steps, require approval for writes, log every tool call, and score it on 30 end-to-end tasks.
Proves: You can make an agent safe and testable, not just impressive once.
A model comparison write-up
Run the same eval set on three models (one small, one large, one open-weight). Compare quality, p95 latency and cost per 1,000 requests, and recommend one with reasons.
Proves: You choose models with evidence, which every AI engineer post asks for.
Free AI Engineer resume template (Word) with a sample
One Word file in the one-page table format that Indian recruiters, placement cells and founders know, filled with a made-up example for this role. Replace every line with your own.
Education with marks, experience with a label beside every bullet, co-curriculars, extra-curriculars, positions of responsibility and skills. One page.
Opens in Microsoft Word, Google Docs and LibreOffice. Everything is real text (no text boxes or images), so applicant tracking systems can read it. We left out photo, age, gender and date of birth on purpose: recruiters do not need them.
SAMPLE: every name, company, college and number is made up. Replace each line with your own.
Ananya Reddy | ananya@example.com | +91 98765 XXXXX | linkedin.com/in/example | github.com/example | Hyderabad
AI Engineer, LLM applications
| Educational Background | |||
| College / Board | Marks | Achievement | |
| B.Tech, CSE | Hyderabad Institute of Technology (fictional) | 8.8 / 10 | Thesis on retrieval for Telugu text |
| Class XII | Telangana State Board | 96.1% | JEE Advanced qualified |
| Class X | CBSE | 95.4% | |
| Professional Experience | |
| Fabrikam Support CloudAI Engineer(2024 – present) | |
| RAG system | • Built a support copilot with hybrid retrieval over 2,400 help articles; 300 agents use it daily and 84% of drafts are sent with light or no edits. |
| Evals | • Built a 300-example eval set from real tickets with support leads; it caught 6 regressions before release in its first year. |
| Latency/cost | • Cut p95 latency from 7.2s to 2.5s with streaming and parallel retrieval, and cost per draft by 45% by routing short tickets to a smaller model. |
| Safety | • Added prompt-injection checks, a provider fallback and tracing; zero data-leak incidents across 1.4 million requests. |
| Tailspin PaymentsSoftware Engineer(2022 – 2024) | |
| Backend | • Built Python and FastAPI services for payment reconciliation, handling 900,000 transactions a day. |
| LLM tooling | • Shipped an internal LLM tool that labels payment-failure reasons; 91% agreement with the ops team on 500 labelled cases. |
| Tailspin PaymentsML Engineering Intern(May '21 – Jul '21) | |
| Classification | • Trained a fraud-alert classifier that cut false positives by 18% on a held-out month |
| Co-curricular and Projects | ||
| Open eval | • Published an open eval of 3 embedding models on Indian-language support queries | 2025 |
| Kaggle | • Top 5% in a Kaggle NLP competition (2,100 teams) | 2023 |
| Course | • Completed the Anthropic API and OpenAI agents courses; built 2 agents with tool calling | 2025 |
| Extra-Curricular Activities | ||
| Speaking | • Talk on evals for RAG at a community AI meetup (200 attendees) | 2025 |
| Teaching | • Teaching assistant, college machine learning course (90 students) | 2021 |
| Sport | • State-level badminton, under-19 | 2017 |
| Positions of Responsibility | ||
| AI club | • Founder, college AI reading group: 30 members, weekly paper discussions | 2020–22 |
| Hackathon | • Organiser, college hackathon with 250 participants and 6 sponsors | 2020–21 |
| Skills and Tools | ||
| Skills | Python, TypeScript, SQL · RAG: chunking, hybrid search, reranking · Agents, tool calling and MCP · Evals and LLM-as-judge · FastAPI, Docker, AWS · Tracing and monitoring | |
Cover letter and LinkedIn
Short and specific beats long and generic.
Hi [Hiring manager name], I am applying for the AI Engineer role. At Fabrikam I built the support copilot that 300 agents use every day. The part I am proudest of is the eval suite: 300 real tickets that every prompt and model change must pass. It caught 6 regressions before they reached users, and it let us cut cost per draft by 45% with confidence. I tried [Company]’s product and noticed [specific behaviour, e.g. slow answers on long documents]. I would start by building an eval set from real user inputs, then work on latency and cost against it. I wrote a short note on how I would approach it: [link]. I would love 20 minutes to walk you through it. [Your name]
AI Engineer | Production LLM apps: RAG, agents and evals | Python, FastAPI
I build LLM features that people rely on every day. Most recently I built a support copilot used by 300 agents, with an eval suite that gates every change, and cut its p95 latency from 7.2 seconds to 2.5. I care about measured quality, fast responses and systems that fail safely.
Mistakes that get resumes rejected
- Listing frameworks with no result (“LangChain, LlamaIndex, CrewAI, AutoGen”)Fix: Name a tool once, inside a bullet that shows what you built with it and how it performed.
- Accuracy claims with no eval setFix: Say what it was measured on: how many examples, where they came from, and how they were scored.
- Only notebooks and tutorial clonesFix: Deploy one project behind an API with logging and link it. Production is the job.
- Ignoring latency and costFix: Put p95 latency or cost per task in at least one bullet. Every post we checked asks for it.
- Two-column, icon-heavy templatesFix: Use one column with clear headings. Recruiters skim, and parsers misread columns.
Who to ask, what they should say, a sample letter and the email to send.
Keep going
Frequently asked questions
What should an AI engineer resume show that a software engineer resume does not?
Evals and trade-offs. Show how you measured quality (an eval set, a hallucination rate), the latency and cost you hit, and why you chose a model or approach. BambooBox’s Senior AI Engineer post in Bengaluru, for example, lists proven experience designing LLM evaluation frameworks as a requirement, not a bonus.
I am a fresher with no AI job experience. What do I put on my resume?
A projects section with one deployed RAG app and one small agent, each with an eval table: how many test questions, accuracy, latency and cost. Link the repo and a live demo.
Should I list every framework I have used?
No. Name the tools a post asks for only where you really used them, inside bullets. Workfall’s AI Engineer post in Bengaluru names LangChain, LlamaIndex and several vector databases, but one bullet that shows a result with one of them beats a list of all of them.
Is an AI engineer resume the same as an ML engineer resume?
Not quite. ML engineer resumes lead with training and serving models. AI engineer resumes lead with applications on top of foundation models: retrieval, agents, evals, latency and cost. If you have done both, put first the one that matches the post.
How long do recruiters spend on a resume?
A 2018 eye-tracking study by Ladders found recruiters spend about 7.4 seconds on the first skim. Simple one-column layouts with clear headings and bulleted results held attention best.