Natansh Badolia

Immediate Joiner | AI Automation Engineer | AI Agents • RAG Systems • Local AI • Computer Vision

Greater Delhi Area

About

AI Automation Engineer and Computer Vision Engineer with a B.Tech degree from Delhi Technological University (DTU), passionate about building real-world AI systems across computer vision, agentic workflows, and retrieval-augmented generation (RAG). I enjoy building practical AI applications that solve real problems, including multi-camera vision systems, intelligent video analytics pipelines, local AI assistants, document Q&A systems, and agentic workflows powered by LLMs. Previously worked as a Computer Vision Engineer at NAYAN AI, where I developed and deployed AI systems involving object detection, person search, CCTV analytics, RTSP-based pipelines, and edge inference using models such as YOLO and ArcFace. My recent work has been focused on: • AI Agents & Tool-Use Systems • RAG Pipelines & Document AI • Local AI using Ollama & Open-Source LLMs • Workflow Automation • Multimodal AI Systems • Computer Vision & Video Analytics I enjoy working at the intersection of AI engineering, automation, and practical product development, with a strong focus on building deployable and scalable systems. Always interested in building impactful AI solutions, collaborating on innovative ideas, and exploring practical applications of artificial intelligence.

Experience

  • NAYAN (On-site)
    • Computer Vision Engineer
      May 2025 - Apr 2026 · 1 yr

    • Computer Vision Engineer Intern
      Jan 2025 - Apr 2025 · 4 mos

  • Machine Learning Engineering Intern at Reliance Jio -Saurashtra (region)
    Jun 2023 - Jul 2023 · 2 mos

    I developed a machine learning model that groups images with similar faces together using deep learning-based face detection, feature extraction, and clustering algorithms. The project involved creating a feature extraction mechanism and applying clustering algorithms to effectively cluster images based on facial similarities. A custom face dataset was created for this project using YOLOv8, a deep learning object detection model. Different PyTorch models were analyzed for feature extraction like ResNet18, VGG16, DenseNet161.

  • Science and Research Department at INFERNO DTU
    Dec 2020 - May 2023 · 2 yrs 6 mos

    In charge of gathering and analysing data from sensors and chemical tests performed on the rover.