i build backends
that don't break,
3× faster.
Full-stack developer · 3+ years shipping production-grade AI and SaaS products. I pair production discipline with AI-augmented workflows (Claude Code, Cursor, MCP) to ship features faster than teams 3× my size.
don't read it. ask it.
This isn't a chatbot widget. It's a working agent loaded with my CV, every project I've shipped, the stacks I've used, the metrics I've hit, and how I work with clients. Ask anything — pricing, architecture decisions, specific projects, fit for your team. Real answers, no recruiter dance.
what numbers look like
when boring code pays off.
Real-Time Presentation & Document Engine
High-performance AI presentation and document generation platform capable of real-time multi-slide streaming, complex multi-sheet Excel ingestion, and pixel-perfect PDF/PNG export. I architected the streaming parser, normalized auto-save pipeline, LLM context compressor, and headless render service.
Monolithic Payloads, Latency Spikes & Sluggish Canvas UI.
Generating multi-slide presentations led to blank-screen wait times of over 20s. Unsanitized HTML blobs with raw floating-point coordinates (e.g. 142.8934px) bloated DB storage. PDF exports locked node processes and caused layout shifts.
- →20s+ initial wait for generated slides
- →Bloated raw HTML payloads crashing DB storage
- →Node event-loop blocks during PDF exports
- →Unsanitized HTML leading to token leakage in error paths
Normalized Auto-Save + Context Compression + Render Pool.
Rebuilt the entire generation and rendering stack around three key pillars: an optimistic HTML normalization engine, a 14-tool agentic backend with Cerebras LLM context compression, and an isolated Puppeteer render pool for pixel-perfect PDF exports.
- →Normalized auto-save layer(strips bloat & rounds float coords)
- →14 native LLM tools (HtmlGrep, line-range view, sandbox execution)
- →Context compression layer via Cerebras / gpt-oss-120b
- →Dedicated Dockerized Puppeteer render pool (dynamic viewports)
Sub-3s Slide TTI, 70% Less Storage & 4x Ingestion Speed.
Reduced Time-to-Interactive from 20s+ down to <3s. Database storage per deck shrank 70% by stripping inline class bloat and rounding spatial values. Multi-sheet Excel and multi-asset ingestion became 4x faster with zero export layout shifts.
- →85% user perceived wait time(TTI under 3 seconds)
- →-70% DB storage footprint
- →-50% redundant auto-save calls
- →4x asset upload throughput
- →-60% PDF/PNG export render latency
- →0 auth/token leakage incidents
how i ship features 3× faster
without breaking prod.
A real replay of how I drive Claude Code through a feature ticket. Spec → plan → implementation → tests → PR — with me in the loop on every architectural decision, not babysitting boilerplate. Press ▸ play below.
production projects, indexed.
tools i reach for every day.
five years of shipping things that didn't break.
- →Spearheaded real-time AI presentation engine using Next.js 15 and Streaming Markdown — enabled under 3s Time-to-Interactive and cut perceived wait time by 85%.
- →Engineered custom HTML normalization + sanitization for auto-save — reduced database storage requirements by 70% and redundant API saves by 50%.
- →Architected a parallel document processing pipeline using S3 presigned URLs and batch uploads — reduced processing time by 60% and increased asset throughput by 4x.
- →Built and deployed production-grade AI applications using Next.js, React, and Node.js with LLMs, vector databases, and real-time streaming.
- →Designed and scaled backend systems using Redis, PostgreSQL, and MongoDB for efficient data processing, semantic search, and low-latency caching.
- →Led end-to-end development of AI email, semantic search, and event platforms — owning architecture, performance optimization, and UI/UX.
- →Led front-end development for scalable web applications serving 5,000+ users in the first month.
- →Implemented conversion-optimized UI/UX improvements that contributed to 2.5x sales growth.
- →Increased team productivity by 70% through research and new strategies for derivative opportunities.
- →Analyzed market trends and crafted hedging strategies for options and derivatives.
- →Led a 10-member technical analysis team focused on options derivatives research.