Self-taught full-stack engineer who designs and ships complete products from zero to production, combining frontend/backend expertise with deep ML fundamentals (transformer architecture, LLM training). Built a 15.5K-line conversational AI platform live in production and trained a GPT-style language model from scratch—bridging product velocity with machine learning depth.
Route to AI/ML Engineer, AI Product Engineer, Consultant teams building high-throughput services in the candidate's core stack.
End-to-end product shipping: zero-to-production TypeScript/React/Node monorepos with CI/CD. LLM/AI integration & cost governance: Claude API, conversation memory, spend metering, prompt architecture. Machine learning fundamentals: transformer architecture, tokenization, training loops, PyTorch from scratch. Real-time systems: WebSockets, Socket.io, WebRTC signaling, multi-user synchronization.
Backend Engineer, AI/ML Engineer, Full-Stack Engineer, Product Engineer.
Remote-first. Hybrid considered for high-alignment local roles. Based in St. Petersburg, FL, US.
Targeting $100K+. Currently: Software Engineer.
Recruiter screen → hiring-manager review → technical conversation around CreatorVault escrow marketplace, Data pipeline & web scraping, Echoes of History conversational AI platform.
Glitz's work in Backend Engineer, AI/ML Engineer, Full-Stack Engineer translates directly into adjacent domains with similar architecture, scale, and reliability needs.
This reusable proof index maps common senior/staff engineering requirements to candidate proof. Role-specific addenda extend — not replace — this base matrix.
Requirement: Deliver on creatorvault escrow marketplace at production scale.
Proof: Next.js 16/React 19/Prisma 7 MVP with KYC, Stripe-ready payment abstraction, buyer/creator roles
Requirement: Deliver on data pipeline & web scraping at production scale.
Proof: 8.5M-token corpus from 10 heterogeneous sources with robots.txt enforcement, per-domain rate limiting, resumable crawl state
Requirement: Deliver on echoes of history conversational ai platform at production scale.
Proof: 15,500 LOC TypeScript monorepo, zero to production in 6 weeks, live in production with Claude API cost governance
Requirement: Deliver on gpt-style language model training at production scale.
Proof: Decoder-only transformer from scratch, 8.5M-token corpus, validation loss reduced from 9.7 to 4.0
Requirement: Deliver on guildev governance platform at production scale.
Proof: Live demo deployed on Vercel
Requirement: Ship work that requires end-to-end product shipping: zero-to-production typescript/react/node monorepos with ci/cd.
Proof: Demonstrated at prior roles.
Next.js 16/React 19/Prisma 7 MVP with KYC, Stripe-ready payment abstraction, buyer/creator roles
8.5M-token corpus from 10 heterogeneous sources with robots.txt enforcement, per-domain rate limiting, resumable crawl state
15,500 LOC TypeScript monorepo, zero to production in 6 weeks, live in production with Claude API cost governance
Decoder-only transformer from scratch, 8.5M-token corpus, validation loss reduced from 9.7 to 4.0
Live demo deployed on Vercel
Mid — role fit: adjacent.
Mid — role fit: primary.
Mid to Senior — role fit: primary.
github.com/Glitzencode — public repositories, contribution activity, and open source project surfaces.
https://pypes.dev/glitz-litzenberg
Hosted candidate portfolio with positioning, proof points, and conversion kit surface for recruiter routing.
Professional profile and recruiter connection path.
This kit is designed to survive internal forwarding. The fastest next step is a recruiter screen, hiring-manager review, referral handoff, or direct call.
Primary fit: AI/ML Engineer, AI Product Engineer, Consultant.
Fastest way to reach Glitz — grab a slot on their calendar.
Persistent candidate surface for routing, resume, and proof surfaces.
Professional profile and recruiter connection path.
Public engineering surface, repositories, and OSS project path.
Additional candidate-owned portfolio and technical writing surface.
Glitz Litzenberg is a St. Petersburg, FL, US-based AI/ML Engineer. Self-taught full-stack engineer who designs and ships complete products from zero to production, combining frontend/backend expertise with deep ML fundamentals (transformer architecture, LLM training). Built a 15.5K-line conversational AI platform live in production and trained a GPT-style language model from scratch—bridging product velocity with machine learning depth. Best fit: AI/ML Engineer, AI Product Engineer, Consultant.