AI/ML Engineer - RAG & Generative AI

Anuj Kumar

+91-902******* **************@*****.*** PortFolio LinkedIn GitHub LeetCode

PROFESSIONAL SUMMARY

AI/ML Engineer with hands-on experience building intelligent systems using Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI. Skilled in Python, Retrieval-Augmented Generation (RAG), and semantic search pipelines, with experi- ence training and evaluating machine learning models. EDUCATION

GLA University, Mathura May 2023 – May 2027

B.Tech in CSE (Hons.) with Specialization in AI & Data Analytics SKILLS

AI/ML Machine Learning, Deep Learning, Neural Networks (CNNs, RNNs, LSTMs), Computer Vision, NLP, Transformers, Model Evaluation, Hyperparameter Tuning, Transfer Learning Generative AI & LLMs Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Fine-Tuning, Embeddings, Vector Databases (FAISS)

Frameworks/Libraries TensorFlow, Keras, scikit-learn, MediaPipe, OpenCV, Hugging Face Transformers, LangChain, NumPy, Pandas, Streamlit

Programming Python, Java, JavaScript, SQL

Tools Git/GitHub, Visual Studio Code, Google Colab Fundamentals Data Structures & Algorithms, Object-Oriented Programming (OOP) PROJECTS

Veritas: Multi-Agent RAG-Based Fact Verification System GitHub March 2026 Role: AI Engineer

Python, RAG, LangChain, FAISS, Transformers, Wikipedia API, Gemini API, Pydantic, PyPDF2 Engineered a multi-agent RAG-based fact verification system that performs claim extraction, semantic retrieval, evidence rank- ing, and automated verdict generation using retrieved knowledge from Wikipedia. Implemented semantic chunking, vector search, andMMR-based retrieval to improveevidencerelevance,generating evidence- grounded verdicts with confidence scores while reducing hallucinations through retrieval-augmented generation. Sauron: Traffic Management System GitHub Dec 2024 Role: AI Engineer

Machine Learning, Random Forest, Python, Streamlit Designed and trained a Random Forest model to dynamically optimize traffic signal timing at a four-way intersection, reducing static wait-time inefficiencies achieving an R score of 0.977. Implemented YOLO-based real-time vehicle detection to classify high- and low-traffic lanes, feeding live signal-timing decisions. CERTIFICATIONS

Microsoft Certified: Azure AI Fundamentals (AI-900) May 2024 Microsoft

CS50P: Introduction to Programming with Python June 2024 Harvard University

Machine Learning with Python June 2025

FreeCodeCamp

Project: Book Recommendation System – KNN GitHub Supervised Machine Learning: Regression & Classification July 2025 DeepLearning.AI — Coursera

COMPETITIONS & EVENTS

Reckon 6.0 [Top 10] March 2025

JIET, Jodhpur

RPC (Rapid Prototyping Camp) August 2024

SJB Institute Technology, Bangalore organized by Intel Unnati Edulateral Foundation Tech Fest February 2025

GLA University, Mathura

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