Open to Work

Hello, I'm Harin.

Applied AI and robotics engineer, currently Master's in Robotics at the University of Birmingham with a focus on Machine learning, Computer vision and Development. Building end-to-end systems from data to deployment across domains.

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Currently working

Robotics & AI Engineer

01

Practicing development of production ready ML APIs for deployment

02

Learning multimodal RAG pipelines with vector databases

03

Exploring cybersecurity considerations for robotics

04

Strengthening backend development fundamentals

Stack_Overview

Languages

Python

C++

SQL

Dart

Machine learning & Computer vision

NumPy

Pandas

Scikit-learn PyTorch

PyTorch

TensorFlow OpenCV

OpenCV

Detectron 2

Roboflow

LangChain

W&B HuggingFace YOLO (v5/v8)

Infrastructure & Deployment:

Git/GitHub ONNX

ONNX Runtime FastAPI

FastAPI

Flask

Docker

AWS S3

PostgreSQL MLflow

MLflow

Streamlit

Gradio

Supabase

Postman

GitLab

Robotics

ROS 2 Gazebo Unity

Unity Carla Webots

Raspberry Pi

Flagship_Projects

Real-Time Bubble Detection Pipeline

Edge AI

Developed an end-to-end computer vision pipeline for detecting, counting and measuring bubbles in reactor flows for continuous quality control in advanced manufacturing. The system uses YOLOv8 segmentation for precise bubble detection. The model is then Optimized by Quantization for real-time deployment on a Raspberry Pi 5. The goal is to Provide a low-cost, production-ready alternative to traditional industrial vision systems.

Roboflow PyTorch YOLOv8 Segmentation Model Training Quantization ONNX Export Real-Time Inference Raspberry Pi 5 Edge Deployment End-to-End Deployment

GitHub repo

Vision-Based Robotic Sorting

Featured

Design and implement a vision-guided robotic pick-and-place system for automated fruit sorting using a Quanser QArm and Intel RealSense RGB-D camera.

Developed a YOLOv5-based object detection pipeline to classify strawberries, tomatoes, and bananas with 96.3% precision and 94.3% recall on custom datasets (12k+ images).
Implemented depth-based 3D localization, camera-to-robot coordinate transformation and inverse & forward kinematics to compute accurate grasp poses for real-world manipulation.
Built autonomous modes and integrating motion planning, gripper control and Simulink-based HIL testing to validate robot behavior before hardware deployment.

Future Work: Planned to integrate a conveyor belt and design a custom robotic arm and end-effector from scratch, enabling optimized industrial automation by connecting computer vision, machine learning and robotic manipulation for real world industrial purpose.

Open CV YOLOv5 Forward & Inverse Kinematics End effector Model Training & Evaluation Inference Optimization Depth Estimation Motion Planning Simulink MATLAB

6k+ Youtube views Technical Document.pdf

CBDC Fraud Detection

Fintech

Built an end-to-end machine learning system to detect fraud patterns in Central Bank Digital Currency (CBDC) transactions. The project simulates realistic digital-currency transaction data and evaluates multiple ML models to identify circular and anomalous fraud behaviors in experimental digital currency systems which is currently in experimental in the European Union.
To Implement a production-style pipeline with experiment tracking, model evaluation and a Fast API based inference service, containerized using Docker for deployment.

XGBoost Fraud Detection End-to-End ML Pipeline Synthetic Data Generation Data Preprocessing Feature Engineering Evaluation Metrics Fast API Docker Containerization

GitHub repo

Other_Projects

Brain MRI Segmentation

Implemented and evaluated classical computer vision–based segmentation methods (Sobel, Canny, thresholding, and K-means) on 3D brain MRI volumes to isolate anatomical regions. Performed slice-wise and volumetric segmentation, and quantitatively compared methods using Dice Similarity, IoU, Pixel Accuracy, and Adjusted Rand Index (ARI) against ground-truth labels to identify the most effective approach.

Python OpenCV Scikit-learn Image Registration Medical Image Analysis Evaluation Metrics

Technical Report

Hackathons

Paper Pilot

Google_Hackathon_2025

Paper Pilot is a Chrome extension that helps students and researchers work with academic PDFs directly in the browser. It extracts text from research papers and uses built-in browser AI APIs to generate concise summaries and simplify complex academic content, improving readability and productivity without leaving the PDF view.

The extension integrates PDF.js for reliable text extraction, Chrome Extensions APIs for tab and side-panel management, and Chrome’s experimental AI APIs for in-browser summarization and rewriting. Designed with a clean, professional interface, Paper Pilot works across different PDF viewers and embedded documents while keeping all processing within the browser.

GitHub repo YouTube

YouTube Demo

Career_Log

Research Assistant (Computer vision)

University of Birmingham

Aug 2025 – Dec 2025

Collaborated with PhD researchers on Taylor bubble flow analysis, developing a custom Mask R-CNN–based segmentation pipeline achieving ~88.6% mAP for precise bubble detection in fluid dynamics experiments.
Built an end-to-end computer vision system combining instance segmentation, OC-SORT multi-object tracking, contour analysis, and edge-based slug length measurement, enabling automated extraction of bubble metrics.
Deployed a Gradio-based analysis web app for CSV export of bubble radius, width, and slug length metrics for research analysis.

Computer Vision Pipeline Deep Learning Segmentation Models Mask R-CNN OC-SORT Contour Extraction Model Inference Gradio Interface

GitHub repo

Data Analyst Intern

T-FAB Technologies LLP

Jun 2022 – Nov 2022

Queried SQL to extract, aggregate, and validate purchase and operational data from company databases, and performed exploratory data analysis and data wrangling to identify trends in sales and raw material
Built interactive dashboards using Excel and Tableau to visualize equipment utilization and cost-related metrics for reporting and analysis.

Excel SQL Pandas Matplotlib Seaborn Beautiful Soup Tableau

Engineering Intern

Hyundai India

Jan 2022 – May 2022

• Assisted in the development and testing of automotive cruise control systems, including sensor calibration and embedded ECU integration for real-world vehicle testing.

Teamwork Hardware–Software Integration Sensor Calibration System Validation Communication

Academic_Stack

M.Sc. Robotics

University of Birmingham

2024 - 2026 Expected Graduation - 2026

Courseworks

Machine Learning Computer Vision Natural Language processing Robotic Path Planning Forward & Inverse Kinematics

B.E. Mechanical Engineering

Karpagam Academy of Higher Education

2018-22

Foundation_Stack

Linear Algebra Calculus & Stat Robotics Engineering Graphics Programming (C / Python) Mechatronics

Connect

Medium

Technical blogs on AI and Robotics.

Behance

UI/UX Design experiments.

YouTube

Tech journey documentation.

Discord

Community hub.

About me!

I’m an engineer who enjoys building things end to end, from understanding the problem to deploying systems in the real world.

I’m a first-principles thinker who likes to wear multiple hats, especially at the intersection of AI and Robotics.

Curiosity and creativity is what drives me and my interdisciplinary background gives me an edge in different perspective and an advantage in connecting ideas across domains!