Resume for AI jobs (2026)

Length: 1 page (junior up to ~3 years) or 2 pages max (senior with quantified impact).

Recommended order:

1. Header — Name, precise title, city / remote, email, LinkedIn, GitHub or portfolio.

2. Summary (2–3 lines) — Stack + specialty + measurable outcome.

3. Experience — Most recent first, 3–5 impact bullets per role.

4. AI projects — Dedicated section if little professional experience (career change, bootcamp).

5. Skills — Grouped tags (ML, LLM, MLOps, cloud, data).

6. Education & certifications — Degree, bootcamp, vendor certs.

7. Languages — English expected for most US AI roles.

Skip decorative graphics, multi-column layouts, and photos that break ATS parsing.

"Data scientist" alone is too vague in 2026. Prefer titles aligned with Ganloss listings:

• LLM Engineer · RAG & agents
• MLOps Engineer · Kubernetes / MLflow
• Data Scientist · NLP & evaluation
• Machine Learning Engineer · computer vision
• Data Engineer · analytics pipelines & feature store

Tailor the title to each application: mirror the posting if you have matching experience. Recruiters often filter on exact ATS keywords.

Each bullet should answer: What + How + Result.

Strong examples:

• "Shipped a RAG pipeline (LangChain, pgvector) cutting support response time 40% for 12k tickets/month."
• "Productionized 8 scikit-learn models via MLflow + Kubernetes; weekly drift monitoring, 99.5% SLA."
• "Fine-tuned Mistral 7B for document classification: F1 +12 pts vs baseline, inference cost −30%."

Action verbs: design, deploy, optimize, evaluate, automate, monitor. Avoid "participated in" or "assisted" without technical detail.

Coming from a bootcamp (Le Wagon, DataScientest…) or a career pivot, a Projects section offsets limited professional experience.

For each project (2–4 max):

• Project title + GitHub / demo link
• Business problem in one sentence
• Stack (Python, PyTorch, OpenAI API, Docker…)
• Key metric or learning
• Duration and context (solo, team, Kaggle, freelance)

AI recruiters prefer a documented POC with a clear README over a certificate with no public code.

Align 5–8 proven skills with the job ad. Ganloss reference by profile:

Data scientist — Python, pandas, scikit-learn, SQL, statistics, A/B testing, Jupyter, Spark (bonus).

LLM engineer — Python, LangChain/LlamaIndex, RAG, embeddings, prompt engineering, evaluation (RAGAS), OpenAI/Mistral APIs, vector DB.

MLOps — Kubernetes, Docker, MLflow, CI/CD, Airflow, Terraform, drift monitoring, feature store.

Data engineer — SQL, dbt, Spark, Airflow, Snowflake/BigQuery, modeling, data quality.

Computer vision — PyTorch, OpenCV, detection/segmentation, ONNX, TensorRT.

Do not list 30 tools — ATS and recruiters check consistency with your projects.

• Buzzword wall with no GitHub or metrics.
• Toy datasets only (Iris, Titanic) with no realistic project.
• Same résumé for LLM engineer and data analyst roles.
• Scanned PDF or heavy Canva layout unreadable by ATS.
• Missing remote preference when you only want remote/hybrid.
• Photo or salary on the résumé — often unnecessary or harmful in US tech.
• Weak English when the team and docs are English-first.

[First Last]

LLM Engineer · production RAG | San Francisco / Remote US

you@email.com · linkedin.com/in/… · github.com/…

Summary

AI engineer with 4 years in NLP and LLM deployment. Focus on RAG, evaluation, and inference cost. 3 models in production (B2B SaaS).

Experience

*Company · LLM Engineer · 2023–2026*

• Multi-tenant RAG architecture (Mistral, pgvector) — p95 latency < 800 ms
• Automated evaluation pipeline (faithfulness, citation rate)
• …

Projects

*Customer support agent* — LangGraph, FastAPI, Docker — github.com/…

Skills

LLM: RAG, fine-tuning, prompt engineering · MLOps: MLflow, K8s · Data: Python, SQL, Spark

Education

MS Data Science · University · 2022

1. Run your résumé through Ganloss AI review (market score + recommendations).

2. Target 5–10 listings aligned with your stack via role hubs (/jobs/ingenieur-llm, /jobs/data-scientist…).

3. Turn on job alerts so you do not miss new posts.

4. Prep interviews with the AI interview guide and matching role profiles.