How to Write an AI CV that Beats ATS (UK examples)

TL;DR

You’ll beat Applicant Tracking Systems (ATS) in the UK by writing a clear, keyword-aligned CV that mirrors the job description, front-loads measurable impact, and proves production-grade skills with links to a tidy GitHub & a real portfolio. Keep it two pages max, use simple headings, put your strongest evidence in the top third, and tailor every submission.

Key takeaways

• Use a clean, conventional layout with standard section headings so ATS can parse it.

• Mirror keywords from the job ad in your Skills, Experience & Projects sections.

• Lead bullets with actions and end with measurable outcomes (numbers, latency, cost, accuracy).

• Prove production: CI/CD for ML, monitoring, data quality, model performance in the wild.

• Show UK context: right to work or visa status, security clearance if relevant, UK education terminology, sector specifics.

• Include two or three high-signal projects with links to GitHub & live demos; keep repos readable.

• Export as PDF unless a portal specifically asks for .docx; use a descriptive filename.

• Keep it to two pages for mid-level and one to two pages for junior; research CVs can stretch to two if needed, but still prioritise impact.

Why ATS matters (and what it does)

Most UK employers route applications through an ATS that parses your CV into structured fields and flags relevance based on keyword matches & recency. Good news: you don’t need to “game” it—just write for humans with careful keyword alignment. Avoid graphics-heavy designs, columns that split content incorrectly, and text in images. Plain text, clear headings, and consistent formatting will carry you further than any gimmick.

The anatomy of an ATS-friendly AI CV

Header

Name, city or region (e.g., London or Greater Manchester), email, mobile, LinkedIn, GitHub, portfolio. If visa status is relevant for sponsorship, add a brief note like: “Skilled Worker visa eligible” or “Right to work in the UK”.

Personal profile (3–4 lines)

A confident, specific snapshot focused on your value. Example: “Machine learning engineer with 3 years’ experience deploying NLP & computer vision models to production on Azure & AWS. Strong MLOps practices (CI/CD, monitoring, data drift), with a track record of reducing inference costs & shaving latency. Active open-source contributor.”

Skills

Cluster by category. For example: Languages, ML/AI, Data & MLOps, Cloud & Infrastructure, Testing & Monitoring, Domain Knowledge. Mirror the job ad’s exact phrasing where truthful.

Experience

Reverse chronological. For each role: company, dates, one-line scope, then 3–6 bullets with measurable outcomes. Use sturdy verbs up front, numbers at the end. If you’re junior, your strongest bullets may live under Projects.

Projects

Two or three high-signal projects with concise context, your role, stack, & outcomes. Link to GitHub & a demo or write-up.

Education & Certifications

UK degree naming (BSc, MSc, PhD), institution, year, selected modules if they match the posting. Include credible industry certs (AWS ML, Databricks, Azure AI, GCP ML). Optional: publications, talks, competitions.

Extras that help

Security clearance level (if relevant), hackathons, community contributions, mentoring, teaching, patent filings.

Keywords: how to match them without sounding robotic

• Pull the top 10–15 technical phrases from the job ad (e.g., “PyTorch”, “Azure ML”, “feature stores”, “MLOps”, “LLM evaluation”, “Vector DB”, “Prompt Engineering”, “Databricks”).

• Place them naturally into Skills, your Experience bullets, and Projects where they reflect the truth.

• Use the ad’s phrasing. If it says “Large Language Models (LLMs)”, include that exact phrase at least once.

UK specifics that make a difference

Show impact in UK sectors: finance, NHS/healthtech, retail, defence, gaming, public sector.

• Include right-to-work line if sponsorship is a factor.

• If you have SC or DV clearance or are clearance-eligible, state it succinctly (no sensitive detail).

• Salary expectations are usually not in the CV; keep that for later stages.

• Use UK spelling throughout (optimise, programme, modelling is common in statistics but “modeling” appears in code docs—be consistent).

How to write measurable bullets that impress both ATS & hiring managers

• Accuracy & lift: “Improved fraud model AUROC from 0.78 to 0.86, cutting false positives by 22%.”

• Latency & throughput: “Reduced median inference latency from 240ms to 70ms by quantisation & batch serving.”

• Cost & efficiency: “Cut monthly inference spend by 38% by moving to spot instances & model distillation.”

• Reliability: “Increased model uptime to 99.95% by adding health checks & autoscaling policies.”

• Data quality: “Built feature validation tests that reduced training–serving skew incidents from 6/month to 1/quarter.”

• Time-to-value: “Launched first-gen forecasting model in 8 weeks, enabling 12% reduction in stockouts.”

• Safety & governance: “Introduced bias screening & evaluation harness for LLM outputs, reducing harmful responses by 63% at equal utility.”

GitHub & portfolio tips that actually get clicked

• Curate, don’t dump. Pin 3–6 repositories that match the role you want.

• Make each repo readable: clear README with problem statement, architecture diagram (image file), quickstart, results, and a small “What I’d improve next” section.

• Use branches & issues to show engineering hygiene. Add unit tests for data transforms, model loaders, and inference endpoints.

• Provide a demo: a lightweight Streamlit or FastAPI app; or a notebook with a baked-in small dataset.

• Avoid massive data files in the repo; link to download instructions or use DVC.

• Show productionisation: Dockerfile, CI workflow, basic monitoring hooks, and a short note on deployment.

• Keep it recent. Recruiters look at commit dates.

Common ATS myths (and what to do instead)

Myth: You must include every keyword 10 times.Reality: Two to three honest mentions across Skills, Experience, and Projects is enough.

Myth: Fancy two-column designs score higher.Reality: Many ATS parsers struggle with multi-column layouts. Choose a simple single-column structure with clear headings.

Myth: PDF is always rejected.Reality: Most UK ATS handle PDFs fine. If a portal asks for .docx, comply. Otherwise, PDF preserves layout & prevents accidental corruption.

Myth: Lengthy personal statements help.Reality: Keep your profile tight. Use space for evidence.

Formatting & file-naming best practice

• Font: a standard, readable sans serif (e.g., Calibri, Arial, Helvetica).

• Size: 10–11 for body, 12–14 for headings.

• Margins: normal; keep whitespace.

• Achievements as bullets, not paragraphs.

• Filename: Firstname-Lastname-AI-Engineer-CV-Oct-2025.pdf

Three real UK-style CV examples

Note: Replace placeholders with your details. Keep the structure & tone. Avoid tables & graphics to ensure ATS parses cleanly.

Example 1: Junior AI Engineer (1–2 years or strong projects)

Header

Name SurnameLondon • email@domain.com • 07xxx xxxxxx • LinkedIn • GitHub • Portfolio

Profile

Junior AI engineer skilled in Python, PyTorch & scikit-learn with hands-on experience building & deploying small-scale NLP and CV models. Strong fundamentals in data pipelines, evaluation, and MLOps basics (Docker, CI). Looking to contribute to a production team focused on measurable impact.

Skills

Languages: Python, SQLML/AI: PyTorch, scikit-learn, Hugging Face Transformers, XGBoostData & MLOps: pandas, NumPy, MLflow, Docker, FastAPI, GitHub ActionsCloud: AWS (S3, Lambda, ECS), basic Azure MLPractices: Model evaluation, prompt engineering, experiment tracking, unit testingDomain: E-commerce, customer support NLP

Experience

AI Intern → Junior AI Engineer, Retail Startup, London (Aug 2024 – Present)Scope: Supported the build & deployment of NLP features for customer service.

• Fine-tuned a small LLM for intent classification on 120k UK support tickets, improving F1 from 0.62 to 0.81 & reducing agent routing time by 35%.

• Built a FastAPI inference service with batch endpoints; cut median latency from 180ms to 95ms via token pruning & caching.

• Implemented MLflow tracking for experiments; reduced duplicated runs & time-to-decision by ~25%.

• Created data validation checks that cut training–serving skew incidents from weekly to monthly.

Projects

Product-Image Matcher (Portfolio link + GitHub)• Trained a vision encoder to match product photos to catalogue items; improved top-1 recall from 71% to 84%.

• Deployed a Dockerised endpoint on AWS ECS with autoscaling; reduced 99th percentile latency from 900ms to 280ms.

• Added basic monitoring (error rates, drift proxy) & Slack alerts.

LLM Help Centre Search (Portfolio link + GitHub)• Built a RAG prototype using a vector DB for 4k articles; reduced failed search queries by 43% on a pilot.

• Implemented prompt templates & evaluation harness; cut hallucinations by 38% with guardrails.

Education & Certifications

BSc Computer Science, University of Bristol (2024)AWS Cloud Practitioner (2024) • Hugging Face NLP Course (2025)

Additional

Right to work in the UK • Volunteer mentor, Code Club

Example 2: Mid-Level Machine Learning Engineer (3–5 years)

Header

Name SurnameManchester • email@domain.com • 07xxx xxxxxx • LinkedIn • GitHub • Portfolio

Profile

Machine learning engineer with 4 years’ experience shipping models to production in finance & retail. Focus on MLOps, reliability, and cost efficiency. Comfortable across the stack: data prep, feature stores, training pipelines, CI/CD, & monitoring. Looking to own model lifecycle and mentor juniors.

Skills

Languages: Python, SQL, BashML/AI: PyTorch, LightGBM, CatBoost, Hugging Face, ONNXMLOps: MLflow, Feast (feature store), Great Expectations, Airflow, Docker, KubernetesCloud: Azure (AKS, Azure ML), AWS (ECR/ECS), DatabricksObservability: Prometheus, Grafana, Evidently, SentryPractices: Canary releases, A/B testing, model cards, bias checksDomain: Payments fraud, demand forecasting

Experience

Machine Learning Engineer, Fintech Scale-up, Manchester (Nov 2022 – Present)Scope: End-to-end ownership of fraud detection & authorisation models.

• Rebuilt fraud detection pipeline on Databricks + Feast; lifted AUROC from 0.79 to 0.88 & reduced false positives by 27%, saving ~£410k/yr in chargeback costs.

• Distilled a 7B-param LLM to a 1.3B model for merchant risk notes; inference costs down 44% while preserving 95% quality.

• Introduced canary releases & rollback for models on AKS; reduced incident MTTR from 2 hours to 18 minutes.

• Implemented evaluation suite for drift, calibration, & fairness; blocked two risky releases pre-deployment.

• Mentored two juniors; introduced a weekly “paper-to-production” session improving team adoption of best practices.

Data Scientist, National Retailer, UK (Aug 2020 – Oct 2022)

• Built a gradient-boosting demand model that reduced stockouts by 11% across top SKUs.

• Deployed a batch inference job on Airflow & optimised Spark jobs, cutting runtime by 36%.

• Partnered with merchandising to design interpretable features; improved stakeholder trust & adoption.

Projects

Real-time Authorisation Scorer (GitHub)• PyTorch model with feature store retrieval; achieved p95 latency 60ms on AKS using ONNX & mixed precision.

• Added blue–green deployment workflow in GitHub Actions; zero-downtime updates.

Education & Certifications

MSc Data Science, University of Manchester (2020)Microsoft Certified: Azure AI Engineer Associate (2024)Databricks Machine Learning Professional (2025)

Additional

Right to work in the UK • Occasional speaker at PyData Manchester

Example 3: Research Scientist (Industry) or Research Engineer (PhD or equivalent)

Header

Name Surname, PhDCambridge • email@domain.com • 07xxx xxxxxx • Google Scholar • GitHub • Portfolio

Profile

Research scientist specialising in multimodal learning & efficient inference. Published in top venues, with a track record of transferring methods into production. Interests include retrieval-augmented generation, evaluation of LLMs, and safety alignment. Seeking an industry research role with measurable user impact.

Skills

Core: Deep learning, representation learning, multimodal, generative modelsFrameworks: PyTorch, JAX/Flax, Hugging Face, DeepSpeed, RayEfficiency: Quantisation, distillation, LoRA, TensorRTData & Infra: Weights & Biases, MLflow, DVC, Airflow, Docker, KubernetesEval & Safety: LLM eval harnesses, red-teaming, preference modellingDomain: Document understanding, scientific text, medical imaging (non-clinical research only unless certified)

Experience

Research Scientist, AI Lab, Cambridge (Jan 2023 – Present)

• Proposed a retrieval-augmented approach that improved factual accuracy by 19% on internal evals while cutting context tokens by 35%.

• Led distillation of a 13B model to 3B with LoRA & quantisation; p95 latency down 52% with a 31% cost reduction.

• Co-authored two papers (preprints available) & delivered internal tech transfer enabling a production pilot to 50k users.

• Designed an LLM evaluation harness with adversarial prompts; harmful outputs reduced by 62% at equal pass@k.

Research Engineer (PhD Intern), London (Jun 2021 – Sep 2021)

• Built a multimodal prototype that improved OCR+NLP pipeline F1 from 0.71 to 0.83 on a UK documents dataset.

Education

PhD in Computer Science, University of Cambridge (2022)Thesis: Efficient Multimodal Representation Learning for Document UnderstandingMEng Computer Science, University of Cambridge (2018)

Publications & Talks

• List selected publications with links (avoid long lists; point to Scholar profile).

• Industry talks or invited seminars, if any, with short titles & links.

Selected Projects

Safety-Eval Harness for LLMs (GitHub)• Implemented red-team prompts & preference

modelling; reduced flagged outputs by 48% on an open benchmark.

• Packaged as a pip-installable tool with tests & docs; used by two partner teams.

Right to work & Clearance

Right to work in the UK.BPSS cleared (if applicable).

Project section: what “good” looks like

Each project should be a mini case study in 4–6 lines:

• Problem & user or business impact.

• Your role & the stack (be specific).

• Data size/shape or constraints (privacy, latency, cost).

• Metrics before vs after (AUROC, RMSE, latency, cost, precision/recall).

• A link to code & a short demo.

• One line on what you’d improve next.

High-signal junior project ideas that map to UK roles

• Retail: demand forecasting with intermittent sales; show MAPE improvement vs a naïve baseline.

• Fintech: transaction fraud with imbalanced data; explain calibration, thresholding, & business trade-offs.

• NHS-like text triage: intent classification on synthetic notes; detail de-identification & ethical considerations.

• RAG on public policy documents: show retrieval quality metrics (recall@k) and hallucination reduction with an eval harness.

• Computer vision for shelf detection: latency-optimised inference path with quantisation.

Phrase bank for measurable impact bullets

Use any that truthfully fit your work:

• Improved AUROC from X to Y; reduced false positives by Z%.

• Cut inference latency by X% via batching, ONNX export & quantisation.

• Saved £X/month by right-sizing instance types & distilling models.

• Reduced data pipeline failures from X/week to Y/month with tests & observability.

• Lifted forecast accuracy (sMAPE) by X points across N SKUs.

• Achieved 99.9% uptime across two model services using health checks & autoscaling.

• Reduced hallucination rate by X% at constant helpfulness using guardrails & eval harnesses.

• Shortened time-to-first-model from X weeks to Y by standardising project scaffolding.

How to tailor your CV to a specific UK job ad in 10 minutes

• Highlight the three most critical requirements in the ad (e.g., “PyTorch + Azure ML + streaming”).

• Move any matching achievements to the top bullet in each role.

• Rename a skills subsection to mirror the ad’s phrasing (e.g., “Large Language Models (LLMs)”).

• Add one or two relevant projects above older, less-relevant experience.,

• Prune skills that aren’t used anymore to reduce noise.

• Add a one-line UK sector context if helpful (e.g., “NHS pilot, IG-compliant pipeline”).

• Adjust metrics to align with outcomes the employer cares about (latency for real-time, cost for scale).

• Reorder bullets: biggest quantified impact first.

• Save as PDF with a descriptive filename.

• Cross-check that your CV answers the ad’s “must-haves” within the first half page.

Proof of production: what hiring managers scan for

• A deployed endpoint or batch job you owned end-to-end.

• Monitoring & alerting in place.

• Evidence of handling bad data, drift, & model degradation.

• CI/CD for ML with tests beyond notebooks.• Cost & latency awareness.

• Communication with stakeholders leading to adoption.

Ethics & safety: stand out by showing judgement

• Include one bullet where you identified & mitigated bias or risk.

• Mention model cards, dataset documentation, and sign-offs if you’ve done them.

• If you work with sensitive data, show how you complied with UK GDPR and internal governance without exposing details.

What to cut to stay under two pages

• Long technology shopping lists that you’ve only “touched”.

• Old coursework unless it’s directly relevant.

• Vague soft skills lines without proof (e.g., “Team player”)—show collaboration through actions.

• Decorative elements that confuse parsers.

Mini-FAQ

How long should an AI CV be in the UK?

One page for junior if you can prove enough; up to two pages for most roles. Keep research CVs to two pages by focusing on impact & selected publications with links.

Should I put salary expectations on my CV?

No. Keep that for the application portal or conversations.

Should I include references?

“References available on request” is fine, but not necessary.

Is a summary profile required?

It helps if you make it specific & evidence-based. Avoid generic claims.

Do I need a cover letter?

If the portal allows it, a focused cover letter tailored to the role often boosts interviews, especially in the UK market.

Can I use AI tools to help write my CV?

Yes—but verify every claim, keep facts accurate, and ensure your wording is natural & honest.

Your pre-submission checklist

• The job’s top keywords appear naturally in Skills, Experience & Projects.

• Every bullet ends in an outcome (accuracy, latency, cost, uptime, adoption, safety).

• Links work: GitHub, portfolio, demos.

• File exported as PDF (unless .docx is required).

• Filename is professional and descriptive.

• UK right-to-work or visa note included if needed.

• Typos fixed; UK spelling consistent.

• Two pages or less, with the most important content in the top third.

Final thought

An ATS-friendly CV is simply a human-friendly CV written with clarity, evidence & alignment to the job. Keep the structure clean, prove you can deliver in production, and back your claims with numbers. Do this consistently, and you’ll pass screens, win interviews, & stand out in the UK AI market.