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AI Engineer with less than a year in Generative AI & Machine Learning
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AI Engineer with hands-on experience building Generative AI applications using LLMs and Retrieval-Augmented Generation (RAG). Skilled in semantic search, embeddings, and vector databases to create context-aware systems. Strong foundation in machine learning and deep learning, with a focus on developing practical and scalable AI solutions.
VIT Vellore
M.Sc. · Data Science
August 1, 2024 – June 30, 2026
Atmiya University
Bachelor of Computer Application
August 1, 2021 – June 30, 2024
Fahm Technologies
AI/ML Engineer Intern
January 1, 2026 – May 1, 2026
Ahmedabad, Gujarat, India
Ignite Intern
Machine Learning Intern
May 1, 2024 – June 1, 2024
India
Jupical Technology
Python Technical Training
April 1, 2023 – May 1, 2023
Rajkot, Gujarat, India
RAG-Based PDF Chatbot
June 4, 2026 – Present
Built a RAG-based AI chatbot to enable context-aware question answering over PDF documents using natural language queries. Implemented Retrieval-Augmented Generation (RAG) using LangChain and LLMs to improve response accuracy by leveraging document context. Designed and developed scalable backend APIs using FastAPI for document ingestion, processing, and query handling. Integrated Chroma for semantic search by generating and retrieving embeddings for efficient document similarity matching. Developed an interactive chat interface using Streamlit with session-based chat history management using SQLite.
View ProjectGarbage Segregation
June 4, 2026 – Present
Designed and implemented a multimodal deep learning model that combines image features using CNN and synthetic gas sensor data using MLP to classify waste into biodegradable and non-biodegradable categories (merged from 12 original classes). Analyzed each waste class, preprocessed and cleaned the data and applied data augmentation and feature engineering to enhance model performance, achieving 97% training accuracy and 95% validation accuracy, and validated the model's performance on real-world images. Authored and presented a research paper detailing methodology, experiments, and results at a VIT-AP International Conference.
View ProjectAI-Powered Document & Data Chat Platform
June 4, 2026 – Present
Built a Generative AI chat system for natural language queries on CSV, Excel, PDF, and DOCX files. Integrated LangChain with Gemini API for retrieval-augmented generation (RAG). Used FastAPI for backend with document upload/retrieval/summarize APIs and MongoDB Atlas for persistent user-specific chat history. Used Pinecone vector database for semantic search, Implemented async/await concurrency in FastAPI backend to handle multiple simultaneous user queries without blocking I/O. Containerized backend & frontend into a single Docker image for easy deployment.
View ProjectMLOps - Next Word Predictor
June 4, 2026 – Present
Built an MLOps pipeline with AWS(S3), DVC, MLflow experiment tracking, and CI/CD via GitHub Actions, automating the entire workflow from data ingestion to model versioning and registration. Scraped text data using BeautifulSoup and Requests, trained an LSTM model, and deployed it using Flask inside a Docker container, with robust logging and exception handling for production reliability.
View ProjectCertification of completion of basic technical Python training
Unknown
June 1, 2026 – Present
Data Science Training Program Certificate
GeeksforGeeks
June 1, 2026 – Present
Presentation certificate - "Garbage Segregation Using Deep Learning"
VIT-AP International Conference
June 1, 2026 – Present
Certificate of Code Carnival - An Open Hackathon
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong interest and focus on AI/ML, aligning well with an AI Engineer role. The diversity of projects, from RAG-based chatbots to MLOps pipelines and deep learning for waste segregation, shows a broad technical curiosity and willingness to tackle different challenges. The involvement in a research paper and hackathon indicates a collaborative and innovative mindset. However, the experience is primarily academic and internship-based, which might require some adjustment to a fast-paced, senior-level industry environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a structured methodology in developing AI solutions. The MLOps project highlights an understanding of operationalizing ML models, including logging and exception handling. Participation in hackathons and presenting research papers suggests initiative and communication skills. However, without direct interview data, a comprehensive assessment of soft skills like teamwork, leadership, and adaptability is limited.