AI Engineer.
I'm an AI engineer with 4+ years of experience shipping systems for Fortune 500 clients, including Ahold Delhaize, State Farm, and the Automobile Club of Southern California. Before AI, I worked on distributed systems, so I sit at what I think is the right intersection: backend rigor that makes AI systems reliable, and AI craft that makes them useful.
Some of what I've shipped: Claude Code plugins for developer workflows, a ServiceNow ticket resolution agent that closes L1 tickets end to end, multi source RAG pipelines that pull from Jira, Confluence, and internal docs in a single pass, and fine tuned small language models on domain specific support data so they respond in the client's voice.
I'm a Claude Certified Architect from Anthropic's Foundations program. I also hold Google Cloud's Generative AI Leader certification and TEKsystems' 2023 Rising Star award.
Off duty, I play football on weekends with friends and read novels every chance I get. My dream is to travel the world one country at a time, Europe first. Looking forward to it.

On skip-attention, sliding windows, and why on-device speed comes from measurement, not theory.
Here is a weird idea. If you want an AI model on your phone to respond faster, one of the best things you can do is have it skip steps entirely in some of its layers. On TTFT, sliding windows, skip attention, and letting the device tell you what actually wins.

Q, K, V from scratch. What the softmax is actually softmax-ing over.
A step by step walk through self-attention with runnable PyTorch. Query, Key, Value; the scaled dot-product; softmax normalization; masking; multi-head. If BERT and GPT feel like magic to you, this is where the magic actually happens.
A Claude Code plugin that caches project context locally, so Claude starts every session already knowing your codebase. I got tired of re-explaining my repos, so I built it for my own workflow.
A demo that reads dense legal contracts and flags risky clauses, compliance gaps, and negotiation points. I built it to see how far off-the-shelf RAG and GPT-4 get before they miss the important stuff.
Paste a YouTube URL, ask questions, get answers grounded in the transcript. An afternoon build to feel out the whole LangChain retriever surface: loaders, splitters, embeddings, FAISS.