// Software Engineer · AI Engineer · Builder
M.S. Computer Science · 4.0 GPA · Germantown, MD
_
SCROLL
// 01. About
I'm a software engineer and AI systems builder with 4+ years of production experience and an M.S. in CS (4.0 GPA, Hood College), graduated January 2026. Most of my career has been on systems that actually had to keep working — high-volume pipelines, production APIs, multi-agent platforms.
I don't deploy things and hope for the best. I instrument them, measure them, and fix what's actually broken. Recently I've gone deep on AI engineering: LangChain agents with tool-calling and safety guardrails, RAG pipelines, LLM evaluation frameworks, and the observability you need to trust that an agentic system behaves correctly at 2am when you're not watching.
Open to SWE, AI Engineer, ML Engineer, Data Scientist, and Research roles.
// 02. Experience
// 03. Projects
01
// FOUNDING ENGINEER · 2025 – PRESENT
Production multi-agent platform I designed and built as sole engineer. Real-time Kafka pipeline → LangChain agents with tool-calling, per-session memory, multi-step reasoning, and safety guardrails. Agents would sometimes produce responses that looked correct but were wrong in subtle ways — I built evaluation logic that catches those before anything reaches a user. Full observability: OpenTelemetry, Prometheus, Grafana. Measurement is how you know whether a system is actually working.
02
// FOUNDING ENGINEER · 2025 – PRESENT
I built this because most LLM deployments lack the evaluation infrastructure to detect subtle reliability problems before they affect users. Routes identical prompts across OpenAI, Gemini, and Anthropic. Logs quality, latency, and token cost per run. The core is the evaluation schema — structured A/B comparisons across model versions and prompt templates, designed to distinguish whether a change actually improved output or just shifted the failure mode.
03
// BACKEND ENGINEER + PRODUCT OWNER · 2024 – 2025
Full-stack certification exam prep platform. C# / .NET Core backend with CQRS and Domain-Driven Design, Entity Framework Core, PostgreSQL, React + TypeScript on the frontend. Also led the team as Product Owner — managing the roadmap and architecture alongside doing the engineering. Where I got the most hands-on CQRS and DDD practice in a real project rather than just an exercise.
// 04. Stack
Production tools I use daily. Honest about what I'm strong in vs. familiar with. The orbital visualization on the right shows the full picture — inner ring is where I live, outer is where I'm growing.
// 05. Writing
BERT is powerful but slow and expensive to run in production. The DistilBERT paper introduced knowledge distillation — a smaller student model learns not just from labels, but from how the full teacher model behaves. Three combined loss signals. 40% fewer parameters. 60% faster. 97% of BERT's performance. This paper shifted how I think about practical NLP systems: it's not just about removing parameters — it's about transferring understanding.
Read on Medium →Local functions look like a minor syntactic convenience until you use them properly. This article covers when to reach for them over lambdas, how they interact with closures and captured variables, and the performance implications that actually matter in production. Published on Code Maze, one of the largest .NET engineering publications.
Read on Code Maze →MORE ON MEDIUM
AI systems, NLP research, and the practical side of building things that work in production — not just in notebooks.
Follow on Medium →// 06. Contact
I'm open to SWE, AI Engineer, ML Engineer, Data Scientist, and Research roles. If you're working on something interesting and need someone who ships things that actually work in production — let's talk.
[email protected]