Software engineer across full stack, ML, and the systems underneath. I'm at my best turning messy, ambitious problems into software that's fast, reliable, and genuinely usable.
Open to full-time Software, ML, and AI Engineering roles across the US. Let's build something together.
01 — projects
The index that beats the libraries
Everyone imports a vector search library. I wrote my own in C++ and made it win. It answers nearest neighbor queries 1.8 times faster than hnswlib and 2.5 times faster than FAISS, at 0.999 recall, on a single thread.
view case study →
The LLM gateway that remembers
A gateway that remembers what your model already said. It matches prompts by meaning, not exact text, so 78 percent of requests skip the model entirely and the typical answer comes back in 44 milliseconds. The memory runs on Proxima.
view case study →
The TA that never sleeps
The grading platform I designed after 400+ Java and Python submissions landed on my desk as a TA. Six specialized agents grade in parallel under Temporal orchestration, a confidence gate routes only the shaky grades to a human, and it runs local-first, so student work never leaves the machine.
view case study →
Malware Mugshots
My thesis. I turned malware files into pictures and taught three vision models to recognize the family on sight. They vote together and agree 94 percent of the time across 17 families. It became a co-authored paper, now under review.
view case study →
Describe a vibe, get movies
A two-stage hybrid movie recommender, built the way production systems are. A two-tower neural model retrieves candidates through Proxima, my own C++ vector search engine; a neural ranker reorders them. It answers free-text requests like 'a slow-burn sci-fi like Arrival but funnier' and includes movies in theaters this week.
view case study →
Six Degrees
A social platform that thinks in relationships. It keeps 50 thousand connections in a graph database and answers who knows who in two hops, with profiles and content living in their own store alongside.
view case study →
Cited, Not Guessed
A study assistant that refuses to make things up. Every answer is pulled from real source material and arrives with citations, and it stays quiet when it genuinely does not know.
view case study →
What makes a song a hit
A million messy music records that shared no common keys. I joined them with C++ MapReduce on a Hadoop cluster, then asked the clean data what actually makes a song popular.
view case study →
Show of Hands
Contactless identity from the palm of your hand. Four models study every scan and vote on the verdict, and together they reach 99.75 percent AUC.
view case study →
Zero to shipped in a week
An AI patient assistant that talks back, in text and in voice. I took it from an empty folder to a shipped product in under a week, working entirely AI native.
view case study →
TCP, from Scratch
I rebuilt TCP by hand over raw sockets. The handshake, the sliding window, the retransmissions when packets vanish, all of it, with a live dashboard watching every packet move.
view case study →
02 — experience
Aug 2025 — May 2026
ML Research · Malware Classification · TA for ML, AI & Programming Paradigms
Jun 2022 — Oct 2023
EdTech Platform · 70K+ Users
Nov 2021 — Jan 2022
B2B SaaS Platform
03 — education
MS, Computer Science
Aug 2024 — May 2026
BE, Computer Engineering
Aug 2019 — May 2023
Paper · under review
Image-Based Techniques and Ensemble Soft Voting for Malware Classification
S. Lokhande, F. Di Troia, M. Stamp · 2026
Conference
Comprehensive Big Data Analytics Dashboard on Chats
Presented at ICICN 2023, Mumbai, India
04 — skills
05 — about
I'm fascinated by the gap between a great idea and a system that actually holds up at scale. That gap is where I like to work, building software where performance, reliability, and intelligence meet.
I recently finished my Master's in Computer Science at San José State University. My research on image-based malware classification became a co-authored paper, now under review, and somewhere in that work I fell for building intelligent systems that survive the real world, not just the lab.
Before grad school I was a Software Engineer on an EdTech platform serving 70K+ users, building Python microservices, chasing down latency, and shipping features that thousands of learners actually used. The problems I enjoy most sit right where tough engineering constraints meet a simple, intuitive experience.
Outside of work I like taking systems apart to really understand them. Lately that meant writing a vector search engine from scratch in C++ and an LLM gateway that runs on top of it, just to learn how modern AI infrastructure works under the hood. I ship something small almost every day; building is how I learn.
I'm looking for full-time Software, Machine Learning, and AI Engineering roles where I can build scalable systems that solve real problems. Based in the San Francisco Bay Area and open to relocation.