Welcome to my portfolio

Sahil Regonda

I'm Sahil, and I have over a year of programming experience working with various online, brick-and-mortar, and non-profit companies in Python, Java, JavaScript/TypeScript, and more.

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Sahil Regonda

Sahil Regonda

Software engineer working on full-stack products and the machine learning inside them.

Toronto, Ontario
|
USA & Canada
|
University of Toronto
|3.54 GPA · Dean's List (2x)

CS and Mathematics at the University of Toronto, 3.54 GPA, two-time Dean's List, graduating May 2028. Four engineering internships so far, across multi-tenant SaaS, product quality, and operations automation. On my own time I train and deploy models end to end: a CNN that scores building accessibility from a photo in under two seconds, and a retrieval system over SEC filings that cites its sources or reports that it has none.

Most of my work sits between product code and model code, and I'm comfortable on either side of that line.

The parts I tend to own are the ones that get skipped. Converting a trained model into something small enough to actually deploy, then proving the conversion didn't change its output. Making a permission rule agree across the client, the API, the JWT claims, and the database policy instead of just the first one. Writing the regression test from the production failure that caused it.

What interests me is how a system behaves when it's wrong. That shaped the SEC research assistant, which returns a no-evidence response rather than an unsupported answer, and the roster work at ClubConnect, where a demographic field had to stay readable as a derived age without ever exposing the raw value.

Open to software engineering, AI engineering, and machine learning roles.

Notebook

Sketchpad. Draw, drop in an image, save what you make.
Size 8
Opacity 100%
Paper

Pen · shortcuts: B M H A C E for brushes, L R U O for shapes, G I T S for fill, pick, text, stamp · [ ] resize · Cmd+Z undo · Cmd+S save

Experience

Where I've worked and what I shipped.

Software Engineer Intern, AI-Driven SaaS

Applied Optimal Inc. — ClubConnect

Remote

Jul 2026 - Present(2 mos)

Shipped membership, roster, and permission features on a paid multi-tenant club management platform during its launch month.

Shipped 10+ customer-facing production features across 13 SIPs and 28 authored pull requests, spanning the Flutter client, FastAPI services, and Supabase PostgreSQL schema.

Moved age calculation and filtering out of the client and into PostgreSQL RPCs, reducing roster API latency by an estimated 30%.

The team moved the product from pre-launch to 100+ users during this period.

FlutterDartFastAPIPythonSupabasePostgreSQLRLS/RBACLangGraphLangChainDockerGitHub ActionsPytestJira

Software Engineer Intern

Inclusifai

Toronto, Ontario

Jan 2026 - Mar 2026(3 mos)

Rebuilt the funding tracker from a static list into something founders could run a week from.

Embedded Jitsi calls directly inside funding opportunity cards with join and copy-link actions, wiring meeting-end events so the frame collapses on its own and returns the user to the tracker.

Fixed a parsing bug that rendered stored deadlines a day early, then migrated task due dates from DATE to TIMESTAMP so a meeting could carry an actual time.

TypeScriptReactNode.jsExpress.jsPostgreSQLJitsiOAuth 2.0Google Calendar APIMicrosoft Outlook APISupabaseJest

Software Engineer Intern

PashMotors — VINSecure

Remote

Oct 2025 - Dec 2025(3 mos)

Tested a vehicle theft prevention platform against the paths customers actually take, then helped fix what broke.

Built a 25-combination compatibility matrix, five device and OS environments across five browsers, and audited every major page, auth flow, camera path, and core service against it.

Found account verification emails arriving on Gmail within seconds while silently failing on Yahoo, Outlook, and Proton Mail, a defect that blocked signup before users reached the product at all.

JavaScriptNode.jsExpress.jsHTMLCSSPythonTensorFlowJestPageSpeedQASCRUM

AI Automation Engineer

Ontario Inc.

Windsor, Ontario

Sep 2024 - Feb 2025(6 mos)

Replaced the repeated manual checks that kept five convenience stores running.

Automated an estimated 80% of recurring paperwork and order survey workflows across 5+ stores using TypeScript, FastAPI, PostgreSQL, and scheduled pipelines.

Reduced inventory and schedule review time by an estimated 60% with low-stock and shift-change monitoring built over persisted store data.

TypeScriptReactNode.jsExpress.jsFastAPIFlaskPostgreSQLPrismaSQLAlchemyLangChainLangGraphBashLinuxJestPytestPlaywright

Projects

Built end to end, from model training through to the deployed interface.

AccessiGo

Machine Learning2025

Photograph a building entrance, get an accessibility verdict in under two seconds.

98%
classification accuracy
under 2s
scoring time
68%
smaller model (44 to 14.1 MB)
230
automated tests

A campus map shows where a building is, not which door you can get through. AccessiGo classifies an entrance photo as accessible, uncertain, or not a usable entrance, and saves the result to the user's account for the next trip. The uncertain band is deliberate: ambiguous doors go to human review rather than a guess.

The model is a Keras CNN trained on 256×256 entrance images and evaluated on precision and recall, not accuracy alone. TensorFlow was too large for the target serverless environment, so the trained model exports to ONNX and serves through ONNX Runtime behind a parity check that rejects the artifact if predictions drift past 0.00001. Measured drift came in at 5.96e-08. Migrating the backend from Flask to FastAPI preserved the existing upload contract, so the already-released React Native client kept working without a coordinated deploy.

PythonTensorFlowONNXFastAPIReactReact NativePostgreSQLSupabase

SEC Document Copilot

AI Engineering2026

A filings research assistant that cites its sources or tells you it has none.

25
official 10-K filings
4
cost-aware routing lanes
222
passing backend tests

Financial research punishes confident guesses, so this is built to refuse rather than guess. Ask a question in natural language across 25 official 10-K filings from Apple, Microsoft, NVIDIA, Amazon, and Alphabet, and every claim returns tied to a passage you can open and read.

Citations work as a validation contract rather than decoration. The model can only cite evidence registered for that specific question. The validator maps each identifier back to its canonical passage, checks the quoted support and any financial values, allows one correction attempt, then returns a no-evidence response if the answer still can't be supported.

Retrieval pairs pgvector semantic search with PostgreSQL full-text search through Reciprocal Rank Fusion, so paraphrased questions and exact accounting terms both find their target. SEC tables were the hard part, since a generic HTML converter makes a filing readable while destroying the link between a number and its row label, unit, and period. A deterministic lxml extractor keeps that structure intact and indexes every financial row as its own retrieval chunk.

PythonFastAPIPydanticLangGraphLangChainPostgreSQLpgvectorOpenAI APIReactTypeScript

Trial of the Knight

Game2023

A 2D platformer in Python and Pygame.

A knight moves through shifting magic terrain, traps, and room puzzles. Written without an engine, so the game loop, collision handling, and sprite state machines are all hand-rolled.

PythonPygame

Dungeon Runner

Game2023

A dungeon crawler running on plain HTML, CSS, and JavaScript.

Procedural mazes and puzzles with no framework and no build step. Rendering, input handling, and level state are written directly against the DOM and canvas.

JavaScriptHTMLCSS

Contact

Open to software engineering, AI engineering, and machine learning roles.

Currently available for internship opportunities

Open to Work