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AI Engineer with less than a year in Generative AI and RAG
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Generative AI and Python developer skilled in building AI-powered applications using Python, FastAPI, NLP, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs). Experienced in developing intelligent chatbots, semantic search systems, recommendation engines, and voice assistants using vector embeddings, prompt engineering, and REST APIs. Passionate about designing scalable backend architectures and data-driven AI applications with strong analytical and problem-solving skills.
Gurunanak Institute of Technology
Electronics and Communication · Electronics and Communication
N/A – June 30, 2025
AI-Powered E-Commerce Product Recommendation Chatbot
June 4, 2026 – Present
Built a Python-based chatbot that recommends products from a local CSV dataset based on user preferences like budget, rating, and category. Implemented NLP to understand natural language queries (e.g., “best phones under ₹20,000 with 4.5+ rating”) and used Pandas for intelligent data filtering. Designed with a modular architecture for NLP, data handling, and response generation – all using free, open-source tools without paid APIs.
Voice-Based AI Assistant
June 4, 2026 – Present
Developed an intelligent voice-interactive assistant that listens to user speech, processes it using Google Speech Recognition API, and responds with text-to-speech output. Integrated a custom NLP engin to generate meaningful conversational replies. Implemented continuous listening, command recognition (e.g., "exit" to quit), and error handling for seamless interaction. Demonstrated proficiency in speech processing, real-time input handling, and AI-driven dialogue systems.
Agentic RAG Intelligence Platform
June 4, 2026 – Present
Built a production-grade Agentic RAG platform for enterprise knowledge management using LangGraph, FastAPI, ChromaDB, and GPT-4.1. Designed a hybrid retrieval pipeline combining semantic search, BM25 keyword retrieval, and Cross-Encoder reranking to improve document relevance, search accuracy, and contextual understanding for enterprise queries. Integrated live web search and citation-based response generation to reduce hallucinations and provide trustworthy answers from enterprise knowledge sources. Implemented multi-agent workflows, conversational memory, document ingestion, context compression, and real-time streaming for scalable and low-latency AI-powered knowledge retrieval.
Python Essentials 1
Cisco Networking Academy
October 1, 2025 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong interest and practical application in AI, aligning well with an AI Engineer role. The diversity of projects (voice assistant, RAG platform, recommendation chatbot) indicates adaptability and a broad understanding of AI applications. The use of open-source tools and a focus on modular architecture suggest a practical, resource-efficient approach. However, the candidate's experience level is listed as '0', and they are still pursuing a bachelor's degree, which might impact immediate cultural integration into a senior-level professional environment without prior corporate experience.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking and problem-solving abilities through their project work. Their involvement in Bachpan Prayas suggests teamwork and organizational skills. The project descriptions indicate a proactive approach to learning and applying advanced AI concepts. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.