
Agentic Workflow with LangChain + LangGraph
Walkthrough of an agentic workflow built on LangChain and LangGraph — graph-based orchestration, tool use, and stateful multi-step reasoning.
- LangChain
- LangGraph
- Python
- LLM Agents
Open to opportunities
software engineer building data pipelines
New York, NY · ESTSoftware Engineer @ Axon
5+ years turning messy external data into clean, validated records — with TypeScript, React, Node.js, Python, and PostgreSQL doing the heavy lifting.
I specialise in solving complex integration problems - taking messy, inconsistent external data and transforming it into clean internal records. I've built data transformation pipelines, validation platforms, and developer tools across public safety, healthcare, and supply chain domains.
I've built internal tools adopted by engineering teams, co-founded a startup, and shipped consumer products used in NYC public schools. I'm comfortable owning the full lifecycle - architecture, implementation, and release management - and happiest when I can learn quickly and ship software that makes a real difference.
Data pipeline tooling, an Electron desktop suite, and React Native apps shipped to both stores.

Walkthrough of an agentic workflow built on LangChain and LangGraph — graph-based orchestration, tool use, and stateful multi-step reasoning.
Mobile study app for the AZ-900 Azure Fundamentals certification, available on the App Store and Google Play. Interactive learning with practice questions and exam prep.

Internal QA desktop tool automating regulated study workflows. Electron pairs a React renderer with Node services to pull jobs from secure shares, validate CSV/XLSX files in real time, preview PDFs, and ship signed builds per environment.

Demo of a Supabase-backed mobile experience covering authentication, realtime data, and Expo tooling with React Native.
The languages, frameworks, and platforms I reach for most often.
Notes from recent work — debugging, tooling, and the occasional deep dive.
An OpenAI Agent Escaped Its Sandbox—and AI Had Its Wildest WeekVideo
A benchmark agent found a zero-day in its own package proxy, reached the internet, and pulled the answers from Hugging Face. Long-horizon autonomy moves the unit of safety from the single tool call to the whole trajectory.
How I Built a Local AI Talking Head That Keeps the Real PersonVideo
Instead of regenerating the person, the pipeline keeps the real recording and synthesizes only the pixels inside a mouth mask. SyncNet holds the normal track at a zero-frame offset, and a deliberate 400 ms shift moves it by 10 frames — the evaluator is tracking timing, not faces.
Why AI Agents Fail After the Demo—and What CARS24 Gets RightVideo
CARS24 reports more than a million agent conversation minutes a month while most agent demos collapse the moment the task leaves a clean prompt. The difference is a five-part contract around the model: accepted outcome, bounded permissions, evidence-based routing, trajectory evaluation, and a fallback.
Remotion: The Part Most Tutorials MissVideo
Remotion is the camera and the stage, not the director. The polish comes from four visual rules, three reusable templates, and a review loop that judges rendered frames at phone size instead of the source code.
Why AI Coding Agents Ship Unreviewed WorkVideo
A review approves artifact A, a retry drops artifact B at the same path, and the saved approval still passes because it was never bound to the bytes. A read-only verifier recomputes the digest at the action boundary and refuses the replacement.
/ Let's build
Interested in software engineering roles focused on data integration, developer tooling, or cross-platform product work — especially where data accuracy and reliability are non-negotiable.