ATS-friendly · Real structure · Quantified impact
Sample Resumes
Three resume templates built to clear ATS scanners and impress hiring managers — fresher, mid-level ML engineer, and AI/LLM engineer.
JANE DOE email@example.com | +91-9876543210 | linkedin.com/in/janedoe | github.com/janedoe | kaggle.com/janedoe SUMMARY Aspiring Data Scientist with hands-on experience in supervised learning, NLP, and SQL. Strong foundations in statistics and Python. Looking to apply ML to real business problems. EDUCATION Bachelor of Technology, Computer Science | XYZ University | 2022-2026 CGPA: 8.7/10 | Relevant Coursework: Machine Learning, Statistics, Linear Algebra, DBMS SKILLS Languages: Python, SQL, R ML/DS: scikit-learn, pandas, NumPy, TensorFlow, PyTorch, XGBoost Viz: Matplotlib, Seaborn, Tableau, Power BI Tools: Git, Docker, Jupyter, AWS (S3, EC2), Linux PROJECTS Customer Churn Prediction (Python, XGBoost) — github.com/janedoe/churn - Built end-to-end churn model on 50K customer dataset; achieved 0.89 ROC-AUC - Engineered 24 features; reduced false negatives by 32% vs baseline logistic regression - Deployed via FastAPI + Docker on AWS EC2 Movie Recommendation System (collaborative filtering + content-based) - Hybrid system on MovieLens 100K; RMSE of 0.91 on test set - A/B tested ranking strategies; documented results on Kaggle (top 8%) NLP Sentiment Classifier for Product Reviews - Fine-tuned DistilBERT on 100K Amazon reviews; 92% F1 - Reduced inference latency by 40% via ONNX export and quantization INTERNSHIPS Data Science Intern | ABC Analytics | Jun 2025 - Aug 2025 - Built dashboards in Power BI tracking 12 KPIs, used by 30+ stakeholders - Automated weekly report generation with Python; saved 8 hours/week CERTIFICATIONS Andrew Ng's ML Specialization (Coursera) | AWS Cloud Practitioner | Google Data Analytics
JOHN SMITH email@example.com | linkedin.com/in/johnsmith | github.com/johnsmith SUMMARY Machine Learning Engineer with 4+ years building and shipping models at scale. Strong in deep learning, MLOps, and Python systems. Shipped models serving 50M+ predictions/day at <100ms p99. EXPERIENCE Senior ML Engineer | TechCorp | Mar 2023 - Present - Lead ML engineer for fraud detection platform serving 12M transactions/day - Built real-time inference service in FastAPI + ONNX; reduced p99 latency from 220ms to 78ms - Designed feature store on Feast + Redis, cutting feature retrieval from 80ms to 6ms - Led migration to MLflow for experiment tracking; adopted by 4 ML teams - Mentored 2 junior engineers and ran weekly paper-reading group ML Engineer | StartupXYZ | Jul 2021 - Mar 2023 - Built recommendation system serving 8M users; lifted CTR by 18% over baseline - Productionized 6 models on AWS SageMaker with full CI/CD via GitHub Actions - Implemented A/B testing framework, increasing experiment velocity 3x ML Engineer Intern | BigCo | May 2020 - Aug 2020 - Improved image classification model accuracy from 91% to 94% via data augmentation EDUCATION M.S. Computer Science, Stanford University | 2019-2021 | Focus: ML Systems B.Tech CS, IIT Bombay | 2015-2019 | CGPA: 9.1/10 SKILLS ML: PyTorch, TensorFlow, scikit-learn, XGBoost, transformers, ONNX MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Airflow, DVC Langs: Python (expert), Go, SQL, Bash Infra: AWS, GCP, Docker, Kubernetes, Terraform, Redis, Kafka Observability: Prometheus, Grafana, OpenTelemetry PUBLICATIONS / TALKS - 'Scaling Real-Time Inference at TechCorp' — MLOps Community Conference 2024 - Co-author, 'Embedding Cache Strategies' — Workshop @ NeurIPS 2023
PRIYA SHARMA email@example.com | linkedin.com/in/priyasharma | github.com/priyasharma SUMMARY AI Engineer specializing in production LLM systems. Built and deployed RAG pipelines, multi-agent workflows, and fine-tuned open models serving 200K queries/day. EXPERIENCE AI Engineer | AI Startup | Aug 2023 - Present - Built customer-support RAG over 12M docs (Pinecone + GPT-4o); deflected 41% of tickets - Reduced average LLM cost/query from $0.18 to $0.04 via prompt compression, caching, and routing to smaller models for simple queries - Designed eval harness with LLM-as-judge + human review; uncovered 7 prompt regressions before prod - Shipped agentic workflow using LangGraph for invoice automation; processes 4K invoices/week - Fine-tuned Llama 3.1 8B on internal data with LoRA; matched GPT-4 quality for domain tasks at 1/20th cost ML Engineer | OldCo | Jan 2022 - Aug 2023 - Built classical NLP pipeline for compliance review; reduced manual review hours by 60% - Migrated legacy TF1 models to PyTorch; cut training time 4x PROJECTS Open-source RAG framework — github.com/priyasharma/rag-kit | 1.2K stars LLM eval toolkit blog — priyasharma.dev/llm-evals (8K reads) SKILLS LLM: OpenAI, Anthropic, Llama, Mistral, fine-tuning (LoRA/QLoRA), RLHF basics RAG: LangChain, LlamaIndex, Pinecone, Chroma, Weaviate, hybrid search Agents: LangGraph, CrewAI, function calling, tool use Eval: RAGAS, LangSmith, Helicone, custom LLM-as-judge Infra: FastAPI, AWS Bedrock, vLLM, Docker, Modal, Replicate Langs: Python (expert), TypeScript, SQL EDUCATION B.Tech AI & DS, NIT Surathkal | 2018-2022 | CGPA: 8.9
✨ Rewrite your own bullet to this standard
Paste one bullet from your resume. The AI returns three rewrites — impact-led, technical, and scope-led — plus what it would need from you to make them concrete. It never invents numbers or tools you did not mention; gaps come back as [brackets] for you to fill in.
Universal resume rules
- Keep it to 1 page if you have <5 years of experience, 2 pages max.
- Lead with impact, not responsibilities. 'Reduced X by Y%' beats 'Responsible for X'.
- Tailor keywords to the job description — ATS systems match heavily on this.
- Use a clean, single-column layout. ATS struggles with tables and columns.
- Save as PDF (not image PDF). Test extraction with the ATS scanner on this site.
- List a tech stack section near the top. Recruiters scan it first.
- Include links: GitHub, LinkedIn, portfolio, Kaggle, blog. Make them clickable.
- Order experience by reverse chronological. Most recent role gets the most bullets.
- Use consistent date formats (MMM YYYY) and tense (past for past roles, present for current).