I’m a senior backend engineer and systems architect specializing in AI-driven systems, payment workflows, and production-grade backend architecture.

I design and stabilize complex systems where reliability, data integrity, and deterministic behavior matter.

My work centers on:

• Multi-stage AI workflow orchestration (LLM routing, tool execution, validation layers)
• Stripe and webhook-driven payment systems with idempotent event handling
• API integrations and event-driven architectures
• Data modeling and normalization for scalable SaaS platforms
• Hardening fragile systems before they scale

I currently contribute to a confidential, early-stage AI platform under NDA, working across backend architecture, workflow orchestration, and technical leadership. Public repositories here represent experiments or prior work that can be shared openly.

• AI workflow orchestration (OpenAI-based systems, agent-style pipelines)
• Event-driven systems & webhook validation
• Stripe integrations & multi-step payment flows
• REST / GraphQL API design
• Relational data modeling (Postgres/MySQL)
• Python, Node.js, and TypeScript services
• Logging, observability, and failure recovery design

• ikonPractice — Custom dental SaaS platform
  Backend architecture, API integrations, payment logic, and production deployment for a domain-specific SaaS platform.
• MoodMe (Production Mobile App) — Relationship intelligence platform deployed on iOS and Android
  Contributed to backend architecture and AI orchestration systems powering multi-layer LLM workflows, structured validation pipelines, and stateful conversation logic in a live production environment.
• MoodJi Sports (AI Systems Platform – In Development)
  Backend and AI workflow architecture for a sports-focused emotional intelligence platform, including multi-stage AI routing, structured tone/frequency mapping logic, and deterministic processing layers designed for scalability.

These projects represent independent system design and experimentation outside of client work, focused on building production-style systems and exploring advanced backend and AI architecture patterns.

• VectorMind — Production-style RAG system with retrieval explainability
  Hybrid retrieval (BM25 + vector), MMR re-ranking, LLM reranking, streaming responses, and chunk-level citation tracing with a retrieval debug panel.
  → https://github.com/wckliment/vectormind

• (More projects coming soon — building a focused series around AI workflows, retrieval systems, and backend architecture patterns.)

🔗 LinkedIn: https://linkedin.com/in/wchasekliment

Note: Due to NDAs, not all professional work is publicly visible here. I’m happy to discuss architecture patterns, tradeoffs, and system design decisions in detail.