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.

Data Scientist — Fresher / 0-1 yr

A graduate-level resume showcasing strong projects, internships, and academic ML work. Optimized to clear ATS for entry-level DS roles.

Strong project section with quantified impact
Tech stack clearly listed (Python, SQL, scikit-learn, TensorFlow)
GitHub and Kaggle profiles linked
Coursework + certifications integrated

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

ML Engineer — 3-5 yrs

Mid-level ML engineer resume emphasizing production deployment, MLOps, and scale. Designed to clear ATS for IC4/IC5 ML roles.

Production impact metrics (latency, scale, $$ saved)
MLOps tooling explicitly listed
Cross-functional collaboration shown
Open-source contributions / talks (if any)

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

AI Engineer (LLM/GenAI) — 2-4 yrs

Resume for the new wave of AI Engineer roles focused on LLMs, RAG, agents, and prompt engineering.

LangChain, RAG, vector DBs front and center
Production GenAI applications with metrics
Cost-per-query optimization shown
Open weights + closed models experience

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

      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).

    