Automation Engineer / MCA & MSc Advanced Computer Science
I build systems that remove friction and actually get used — from workflow automation and document AI to edge computer vision. 100+ projects delivered. Currently finishing my second Masters in Leeds.
My instinct is to automate. When I see a repetitive process — a data pipeline, a document workflow, a manual task someone runs every day — I want to remove it. That thread has run through everything from early Fiverr scripts to the full-scale automation systems I've built more recently.
I've worked across the stack — Python, AppScript, REST APIs, GCP, Selenium, document AI — mostly on problems where success has a simple measure: does this save real time for real people?
Originally from Kerala, India. Now based in Leeds, finishing my MSc in Advanced Computer Science. What comes next is still forming — but it points at applied AI, edge systems, and software that earns its place.
Let's talkYOLOv8 nano model deployed on Raspberry Pi with Flask split-inference. Lightweight, real-time detection at the edge — no cloud latency dependency.
Ongoing workflow tooling — API integrations, document processing, and pipelines that cut manual overhead.
A personal ecosystem of tools, micro-automations, and utilities built around real friction in day-to-day work.
A real-world application used daily by a community for event planning, meal distribution management, and announcements. Built for practical operational use — deployed and relied upon, not a portfolio demo.
100+ projects delivered across Google Workspace automation, REST API integrations, document processing, Selenium test suites, and custom business tooling. Long-term collaboration with a UK-based client on AppScript, Document AI, and GCP pipelines.
AI news aggregator pulling from 10+ sources. Filter by category, source, and date — then save stories into personalised digests. Fully client-side, no backend or database.
Business finance platform for loans from $50K–$50M. Covers term loans, equipment finance, and invoice factoring — with 48-hour credit decisions and a full product UI built in Next.js.
Multi-user expense reconciliation platform. Import CSVs, normalise records, and match transactions via rule-based scoring. Includes dashboard analytics and role-based access control.
12 browser-based utilities across 4 categories — text cleaning, data formatting, encoding, and calculations. Everything runs client-side with no server, cookies, or tracking.
MSc dissertation project focused on deploying a YOLOv8 nano model directly on a Raspberry Pi 5 for real-time wildfire and smoke detection. The core work was rigorous benchmarking — measuring detection rate, false positive score, inference latency, and resource utilisation under constrained hardware conditions. The deployment used a Flask-based split-inference architecture to keep processing at the edge without relying on cloud round-trips.
The study concluded with a cloud comparison — evaluating how the edge-deployed model's accuracy and speed trade-offs measured up against cloud-based inference pipelines, making the case for where edge deployment is genuinely viable.
Open to full-time engineering roles, contract work, and interesting freelance projects. If you're building something useful and need someone who ships — I'd like to hear about it.
