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AI Engineer with 1+ years in LLM, RAG, and Agentic AI.
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AI Engineer with experience in Python development and hands-on expertise in building LLM applications, Retrieval-Augmented Generation (RAG) systems, and multi-agent AI frameworks. Proficient in Generative AI, LangChain, LangGraph, CrewAI, prompt engineering, and machine learning. Currently pursuing a Post Graduate Diploma in AI/ML and Agentic AI Engineering at IIT Gandhinagar, focused on building reliable, scalable, and production-ready AI solutions.
IIT Gandhinagar
Post Graduate Diploma in AI/ML & Agentic AI Engineering · AI/ML and Agentic AI Engineering
February 1, 2026 – July 1, 2026
Bharat Institute of Technology
B.Tech · Electronics & Communication Engineering
August 1, 2016 – October 1, 2020
Capgemini
Python Developer
March 1, 2025 – August 1, 2025
Pune, Maharashtra, India
Vaco Binary Semantics LLP
Associate Analyst
November 1, 2023 – October 1, 2024
Gurgaon, Haryana, India
NCERT Class 9 Science QA Chatbot
June 30, 2026 – Present
Developed an end-to-end Retrieval-Augmented Generation (RAG) chatbot using LangChain, Groq-hosted Llama 3, and ChromaDB vector database to answer questions from 12 NCERT Science chapters. Built a complete retrieval pipeline comprising document ingestion, custom PDF layout extraction, tokenization, semantic chunking, Transformer-based BGE-large embeddings, and storage of 700+ document chunks in the ChromaDB vector database for efficient semantic retrieval. Implemented a retrieve → re-rank → generate workflow using dense retrieval, cross-encoder re-ranking, query rewriting, prompt templates, retrieval evaluation, and hallucination monitoring, generating grounded responses with source citations.
Customer Intelligence Platform
June 30, 2026 – Present
Developed an AI-powered application combining machine learning and Retrieval-Augmented Generation (RAG) to analyze 5,000+ customer complaint records. Designed a semantic retrieval pipeline using Gemini embeddings, FAISS vector indexing, hybrid search, prompt templates, dynamic query rewriting, and semantic retrieval to improve search relevance and response quality. Integrated an XGBoost classification model with the RAG workflow through a unified FastAPI service and leveraged MLflow for experiment tracking, model evaluation, and experiment management.
Nexus Supply Co. Operations Assistant
June 30, 2026 – Present
Designed a CrewAI-based multi-agent system using FastMCP to orchestrate Researcher and Writer agents across multiple Model Context Protocol (MCP) tools for automating operational workflows. Built an agent workflow involving task decomposition, planning, prompt templates, tool calling, agent memory, and multi-agent collaboration to generate accurate, citation-backed operational reports. Improved agent reliability by implementing a structured pre-processing layer for secure tool execution, reducing planning failures and validating architecture stability using 28 unit and integration tests with Pytest.
Applied Data & Business Analytics Micro-Degree
Jobaaj
April 1, 2026 – Present
Advanced Python, Advanced SQL, GitHub, NumPy, Pandas
CodeChef
March 1, 2026 – June 1, 2026
Data Engineering Foundations
IBM
July 1, 2025 – Present
Python for Data Science & AI
IBM
June 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a strong interest and practical application in cutting-edge AI/ML and Agentic AI. The diversity of projects (QA Chatbot, Customer Intelligence, Operations Assistant) indicates adaptability and a broad understanding of AI applications. The pursuit of a specialized diploma from IIT Gandhinagar further aligns with a culture of continuous learning and innovation. The professional experience, while not directly in AI, demonstrates a solid engineering foundation in Python and SQL, which are critical for AI development roles. The candidate appears to be a good fit for a role requiring hands-on AI development and a proactive approach to learning and applying new technologies.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving skills through debugging and refactoring Python applications, and root cause analysis. Collaboration with cross-functional teams is also noted. The project descriptions indicate an ability to design and implement complex AI systems, suggesting strong analytical and operational capabilities. However, without specific psychometric or English test scores, a deeper assessment of soft skills like logical reasoning, work attitude, stress handling, and team collaboration cannot be made.