AI Engineer with 1+ years in Machine Learning & Computer Vision
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Highly motivated and results-driven AI Engineer with 1.4 years of combined experience in machine learning, computer vision, natural language processing, and generative AI. Proven ability to develop and deploy innovative solutions for complex problems, from mushroom species identification to drug compatibility systems. Skilled in Python, deep learning frameworks (TensorFlow, PyTorch), and MLOps tools. Eager to contribute expertise in building robust and intelligent AI applications for real-world impact.
IIT Kharagpur
M.Tech · Food Process Engineering
August 1, 2024 – June 30, 2026
JNKVV, Jabalpur
B.Tech · Agricultural Engineering
August 1, 2020 – June 30, 2024
MPBSE
Senior School Certificate Examination
June 1, 2018 – May 31, 2019
CBSE
High School Certification Examination
June 1, 2016 – May 31, 2017
CodeSoft
Artificial Intelligence Intern
July 1, 2025 – August 1, 2025
India
Detection of Starch Adulteration in Milk Powder using Image Analysis (MTech Project)
August 1, 2025 – April 1, 2026
Built a computer vision-based system for starch adulteration detection in milk powder using automated image preprocessing. Performed feature extraction, colour-space analysis, and multi-class classification using ML and image processing techniques. Implemented transfer learning models, with ResNet50 achieved 98.42% and the best ML pipeline achieved 96.17% accuracy. Deployed the optimized inference pipeline on Raspberry Pi 5 for real-time, low-cost food adulteration detection.
Agentic RAG-Based Drug Compatibility System
May 1, 2025 – June 1, 2025
Built an Agentic RAG framework with LangGraph, Ollama and ChromaDB to analyze drug-drug and drug-condition interactions. Engineered a planner-tool workflow supporting multi-step retrieval, validation, and knowledge synthesis for compatibility reports. Developed a Streamlit interface for real-time medication safety checks, generating structured outputs with risk levels and alternatives. Enhanced the reliability and robustness of healthcare AI by integrating reasoning steps and context-backed answers for safer clinical support.
Multimodal Knowledge Retrieval System (Course Project)
March 1, 2025 – April 1, 2025
Architected a multimodal RAG system capable of extracting and reasoning over text, images, and graphs from complex PDF documents. Applied CLIP embeddings with FAISS vector indexing to enable efficient cross-modal retrieval and semantic search across large documents. Integrated LangChain with Google Gemini API to generate precise, context-aware answers through advanced multimodal reasoning. Engineered a modular Streamlit interface enabling highly scalable, efficient real-time querying and document exploration.
Intelligent Nutritional Analysis AI Tool (Term Project)
November 1, 2024 – December 1, 2024
Designed interactive AI web application predicting nutrient scores with 85% accuracy using Random Forest classifier models. Processed extensive dataset of 3.7M OpenFood Facts records using PySpark/Pandas for accurate nutrient-score prediction in Flask app. Deployed highly scalable solution with rigorous 5-fold cross-validation ensuring consistent accurate nutrient composition predictions. Implemented multi-agent orchestration (CrewAI, LangChain, FAISS) for healthier recommendations and context-aware insights.
Supervised Machine Learning: Regression and Classification
DeepLearning.AI | Coursera
June 1, 2026 – Present
RAG using LangChain, LangGraph, and LangSmith
Udemy
June 1, 2026 – Present
Excel Basics for Data Analysis
IBM | Coursera
June 1, 2026 – Present
SQL for Data Science
UC Davis | Coursera
June 1, 2026 – Present
Multi Agent AI System Workshop
Codingninjas
June 1, 2026 – Present
Power BI Workshop
Office Master
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Food Process Engineering combined with a strong focus on AI/ML projects demonstrates a diverse skill set and an ability to apply AI in various domains (food adulteration, drug compatibility, nutritional analysis). The projects are primarily academic, but they show initiative and a broad interest in AI applications. The target role of 'AI Engineer' aligns well with the candidate's project experience and technical skills, indicating a good cultural fit for an innovation-driven environment. The candidate's involvement in competitive activities also suggests a drive for excellence.
Soft Skills & Operational Fit
The candidate's participation in extracurricular activities like Smart India Hackathon, volleyball, and NSS unit volunteering suggests a proactive attitude, teamwork capabilities, and a willingness to contribute beyond academic requirements. The Art of Living Workshop indicates an interest in personal development and stress management, which are beneficial for operational fit. However, the resume does not provide direct evidence of communication skills in a professional setting or specific problem-solving approaches.