Hi, I'm Sid 👋

Engineering lead at Hypotenuse AI, building LLM infrastructure for ecommerce product data at scale.

Work

Apr 2026 - Present

Lead Software Engineer · Hypotenuse AI

Own the technical roadmap and architecture for the product database, enrichment services, generation systems and several other services - scaling infrastructure to handle millions of SKUs daily while actively driving down AI and compute costs.
Building generation feedback loops, model retraining pipelines, and internal AI tooling to improve engineering velocity and output quality across the team.

Jan 2025 - Apr 2026

Senior Software Engineer · Hypotenuse AI

Architected and shipped the end-to-end product data platform - a multi-tenant product database, agentic web enrichment pipeline, and bulk processing infrastructure capable of enriching 100k+ products reliably at scale with sub-second query latency across 2M+ products.
Built a canvas-based image editing engine with snapshotting, standard editing operations, and integrated AI image generation models; shipped custom ProseMirror and TipTap nodes with purpose-built interactions for rich content editing.
Set up robust observability across databases and compute using OpenTelemetry, with monitoring and alerting pipelines wired through AWS Grafana and Prometheus.

Jan 2023 - Jan 2025

Software Engineer · Hypotenuse AI

Built core AI platform capabilities from the ground up - RAG pipelines, LLM streaming infrastructure, vector database integration, rate-limiting systems for generation and external APIs, and a web crawler that periodically indexes configured domains to surface contextually relevant content ideas.
Shipped research-grounded blog generation workflows with inline citations, and architected the platform-wide migration from credit-based to word-based pricing to support flexible monetisation across all generation workflows.
Provisioned multi-region AWS infrastructure via Terraform, reduced image generation costs by 89%, and cut server memory consumption by 99.5% through deep profiling and targeted optimisation.

See the full work history

Selected work

All projects

project MatterIxA tiny deep learning framework, built from scratch to teach the fundamentals. pythondeep-learningautodiff 2020 → 2022 project Learnify AITurn your study notes into an interactive knowledge graph. javascriptknowledge-graphnlpedtech 2020 → 2023 project DetoxA drop-in REST API for multilingual profanity filtering. javamlapihackathon 2021 → 2023 project Newscast APIA simple REST API to fetch news articles for any query. pythonrest-apidatacrawling 2021 → 2022 project ShillingsA mock payments backend for testing client integrations end-to-end. gobackendapisystem-design 2022 project SciML for Knowledge DiscoveryA comparative study of symbolic and sparse regression for scientific ML. researchscientific-mlsymbolic-regression 2022 · ICARCV

Recent writing

All posts

post Startup Post-Mortem: Lessons from My Startup OdysseyLooking back on a startup that didn't make it — what I learned about engineering, business, design, and the small ways things tend to go wrong. startuplessonsdatlite Jan 29, 2023

Selected publications

Google Scholar

ICARCV 2022 Dec 2022

Read

A Comparative Study on Machine Learning Algorithms for Knowledge Discovery

A survey and head-to-head benchmark of symbolic regression methods — Genetic Programming, Deep Symbolic Regression, AI-Feynman, and NeSymRes — on the Feynman-03 and Nguyen-12 datasets. We highlight the strengths, failure modes, and open problems for each family of methods.

S. S. Suseela, Y. Feng, K. Mao

Nanyang Technological University 2022

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Scientific Machine Learning for Knowledge Discovery

Master's thesis at Nanyang Technological University. Investigates how machine learning methods can recover interpretable, closed-form models from data — bridging the gap between black-box prediction and scientific insight.

S. S. Suseela