Rahul Yadav
Keyholder (Exploring Ai Solutions) @Geox
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WORK HISTORY
Keyholder (Exploring Ai Solutions) @Geox
Toronto, ON, CA
Managed daily sales operations, including team coordination and customer support-Independently built an Agentic Retrieval-Augmented Generation (RAG) system for product recommendations and care using LLMs-Collected and processed domain-specific data via web scraping and cleaning pipelines-Implemented document chunking using recursive text splitting with LangChain to optimize context retrieval-Integrated vector similarity search using FAISS for efficient semantic retrieval-Designed multi-step reasoning workflows using LangGraph and LangChain-Built a hash-based change detection system to identify dataset updates and trigger selective re-indexing-Used LangSmith for evaluation, tracing, and prompt optimization-Applied prompt engineering to improve retrieval accuracy and reduce hallucinations.
EDUCATION
Georgian College
Postgraduate Degree, Artificial Intelligence: Architecture, Design and Implementation
The NorthCap University
Bachelor's degree, Electrical, Electronics and Communications Engineering
George Brown Polytechnic
Postgraduate Degree, Applied A.I. Solutions Development
ABOUT RAHUL YADAV
AI Engineer passionate about designing, developing, and deploying real-world LLM-powered solutions. Currently building an agentic Retrieval-Augmented Generation (RAG) system for product recommendations and care, leveraging LangChain, LangGraph, FAISS, and LangSmith for retrieval, reasoning, and evaluation. I specialize in building scalable, multi-step reasoning workflows and retrieval pipelines that are ready for practical deployment.With 4+ years of professional experience in BI, data analytics, and predictive systems (Infosys), I bring a strong foundation in data pipelines, ETL, and analytics workflows, combined with hands-on AI/ML engineering skills. I enjoy solving complex problems and transforming data into actionable AI-driven solutions.I am actively exploring opportunities in AI engineering, machine learning deployment, and RAG/agentic systems, where I can contribute end-to-end solutions from data ingestion to LLM-powered reasoning.
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