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AI Engineer with 1+ years in Computer Vision & NLP.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer specializing in developing and deploying intelligent systems with expertise in Computer Vision, Natural Language Processing, and MLOps. Proven ability to build agentic pipelines, RAG systems, and analytics platforms. Skilled in Python, machine learning frameworks, and full-stack development, with a strong record of hackathon wins and research contributions.
Rajiv Gandhi Institute of Petroleum Technology
Integrated Dual Degree (B.Tech & M.Tech) · Computer Science & AI
August 1, 2022 – June 30, 2027
MultiTV Tech Pvt. Ltd.
AI Intern
May 1, 2025 – October 1, 2025
New Delhi, Delhi, India
FSIL at Georgia Tech.
Research Assistant
May 1, 2024 – October 1, 2024
USA
Meeting-to-Action Agent
June 26, 2026 – Present
Built an agentic pipeline using LangGraph that orchestrates 5 specialized agents (Transcription, Summary, Task Extraction, Calendar, Email) to turn raw meeting audio into structured action items, summaries, and follow-ups. Used OpenAI Whisper for transcription and LLM prompting to extract executive summaries, decisions, and action items with owner, priority, and deadline fields, with Calendar and Email agents running in parallel off the same extracted tasks. Built the full stack with FastAPI + Pydantic on the backend and Next.js on the frontend, backed by PostgreSQL and ChromaDB.
Smash-X - AI-Driven Badminton Analytics System
June 26, 2026 – Present
Built Smash-X, a 4-module badminton analytics system combining computer vision, pose estimation, and a Vision-Language Model for explainable AI coaching feedback from match and training videos. Built a match analysis pipeline with YOLOv5 + ByteTrack for player/shuttle tracking and homography-based court mapping, feeding structured match data into Gemini to auto-generate coaching reports. Built a real-time exercise analysis module using MediaPipe pose estimation with custom rep-counting logic, achieving 55 FPS processing and 94-100% rep-detection accuracy.
Agentic RAG: Document Q&A System
June 26, 2026 – Present
Built an agentic RAG system using a LangGraph Drafter-Compliance agent loop, where the Compliance agent evaluates draft answers against retrieved source chunks and triggers a revision before returning a response. Built a hybrid retrieval pipeline combining BM25 and dense vector search (ChromaDB), merged with Reciprocal Rank Fusion (RRF) to improve recall on exact-match queries that vector search alone misses. Built a layout-aware ingestion pipeline that chunks documents by heading/section structure (PDF blocks, DOCX styles), preserving page/section metadata for precise citations.
Cultural Fit Analysis
The candidate's diverse range of personal projects (agentic systems, sports analytics, document Q&A) and internship experiences (computer vision for fashion, table tennis analytics) demonstrate a broad interest in applying AI across different domains. This diversity, coupled with participation in hackathons, suggests an adaptable and curious individual who is likely to thrive in a dynamic, innovation-driven environment. The academic background in Computer Science & AI further aligns with a strong foundational interest in the field.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and an ability to integrate various advanced AI techniques to build end-to-end solutions. The personal projects demonstrate initiative and a proactive approach to learning and applying complex technologies. The research assistant role at Georgia Tech suggests an ability to contribute to academic research and data annotation tasks, indicating diligence. The hackathon achievements highlight teamwork and competitive problem-solving skills.