Fahim Zaman

About

Computer Science undergraduate with hands-on experience in Java, Python, AWS, Linux, and machine learning, including internships at ONGC and IIT Guwahati. Built projects spanning monitoring systems, computer vision, and cloud deployment, with a strong foundation in software engineering fundamentals.

Experience

• Project Intern
  Oil and Natural Gas Corporation (ONGC) · Jorhat
  Jan 2026 – Feb 2026

  Worked on AI-based monitoring and anomaly detection for high performance computing (HPC) clusters; applied machine learning concepts to analyze system behavior and detect anomalies; gained exposure to real-world industrial systems and performance optimization; strengthened skills in system analysis, problem-solving, and AI applications in production environments.

• TIDF Summer Intern
  IIT Guwahati
  Jun 2025 – Jul 2025

  Developed an AI-driven underwater trash detection system using computer vision; collected and processed image datasets for training machine learning models; built and trained models for trash classification and detection; deployed the solution using Streamlit for real-time detection; collaborated in a team to build an end-to-end ML solution.

Education

• 2027 DU
  B.Tech in Computer Science & Engineering

  Dibrugarh University Institute of Engineering and Technology

  Computer Science · Aug 2023 – Jul 2027

  Computer Science · Aug 2023 – Jul 2027

Skills

Tools / apps / platforms

Projects

• Face Recognition-Based Attendance System
  Java, Maven, Java Swing

  Developed a Java-based facial recognition attendance system to automate student identification and attendance recording. Managed the project with Maven and designed an interactive Swing GUI for registration, attendance management, and system operations.

• AWS Cloud Infrastructure & Deployment Project
  AWS, EC2, S3, IAM, Linux

  Ongoing project designing and deploying a cloud-based application with focus on cloud infrastructure, deployment, security, and scalability.

• Underwater Trash Detection System
  Python, YOLO, OpenCV, TensorFlow/Keras, Streamlit

  Built a computer vision system for detecting and classifying underwater waste from images and video streams. Prepared datasets, trained a YOLO-based object detection model, used OpenCV for image processing, applied TensorFlow/Keras for experimentation, and implemented a real-time detection interface with Streamlit.

• SentinelX – Production-Inspired Monitoring System
  RHEL 7.9

  Designed and built a lightweight service monitoring system for real-time system and service monitoring. Implemented real-time health checks, developed a modular and scalable architecture, and built a lightweight web dashboard for system visibility and health-status tracking.