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AI Engineer with 1+ years in AI-driven systems using Python, PyTorch, and Computer Vision.
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Assessing your cultural and operational fit
M.Tech Computer Science student with strong interest in Artificial Intelligence, Machine Learning, and Computer Vision. Experienced in building AI-driven systems using Python, PyTorch, OpenCV, and Vision-Language Models (VLMs). Skilled in data analysis, deep learning, multimodal AI, and scalable software development with hands-on project experience in explainable AI and intelligent systems.
VIT
M.Tech · Computer Science and Engineering
August 1, 2025 – June 30, 2027
AKTU
B.Tech · Computer Science and Engineering
August 1, 2018 – June 30, 2022
Magic Edtech Pvt. Ltd.
Software Engineer Intern - Android Developer
June 1, 2022 – October 1, 2023
Noida, Uttar Pradesh, India
Explainable Temporal Severity Detection using Vision-Language Models (VLM)
June 24, 2026 – Present
Developed a multimodal AI pipeline using Vision-Language Models (BLIP, OpenCLIP) for explainable incident severity detection. Implemented semantic embedding generation and adaptive multimodal fusion for temporal understanding. Applied deep learning and computer vision techniques using PyTorch and OpenCV for intelligent video analysis. Worked on explainable AI, generative captioning, and multimodal feature aggregation for AI-driven decision systems.
AI-Powered PDF Chatbot using RAG and LLMs
June 24, 2026 – Present
Built a Retrieval-Augmented Generation (RAG) based chatbot for querying information from PDF documents using Large Language Models Implemented semantic search using embeddings and FAISS vector database for context-aware document retrieval Integrated OpenAI API and LangChain for prompt orchestration, response generation, and conversational AI workflows
Cultural Fit Analysis
The candidate demonstrates a strong interest in AI/ML through academic projects, which aligns with an AI Engineer role. The project diversity, ranging from VLM-based severity detection to RAG-based chatbots, indicates a broad interest within the AI domain. The blend of academic rigor (M.Tech) and practical application in projects suggests a proactive learning approach. The previous internship, while not directly AI-focused, shows experience in a professional software development setting.
Soft Skills & Operational Fit
The candidate's professional experience as an Android Developer Intern indicates experience in an Agile environment, collaboration with cross-functional teams, and problem-solving (resolving runtime issues). These suggest an ability to work effectively in a team and contribute to project delivery. However, the provided data does not offer specific insights into communication clarity, stress handling, or leadership potential beyond general collaboration.