I’m a senior full-stack engineer with expertise in building and operating secure, scalable, high-reliability web platforms. My background spans healthcare and large-scale consumer products, where correctness, uptime, and operational discipline mattered.

I’m passionate about the shift toward AI-driven products. To stay ahead of that curve, I’ve been intentionally evolving my full-stack toolkit to include LLM integration, and I’m eager to apply these skills to solve real-world problems. I approach AI the same way I approach any complex dependency: with guardrails, observability, cost awareness, and clearly understood failure modes. I’m particularly interested in where AI should and should not be used in real product workflows.

I share notes and experiments from this work at

• Full-Stack Applications
  End-to-end systems built with TypeScript, React, Next.js, Node.js, Python, and FastAPI, designed to scale, be observable, and be owned long-term. Some projects use LangChain/LangGraph for orchestration.

• AI-Powered Product Features
  Applying LLM capabilities to specific product workflows where they add leverage, with guardrails, evaluation, and clear degradation paths.

• Data Retrieval & Search Patterns
  Structured retrieval pipelines (including vector-based approaches) used to support AI features without compromising reliability or cost.

• Operational Scaffolding
  Error handling, monitoring, evaluation harnesses, and CI/CD that make complex systems — including AI-assisted ones — debuggable and supportable.

AI systems should be:

Observable
Silent failure is more dangerous than loud failure. If you can't detect quality degradation, you don't have a production system.

Eval-first
Define success criteria before building. Assessments constrain content; evals constrain features.

Cost-aware
Every architectural decision has a price tag. Model costs at 10× current scale before shipping.

Failure-tolerant
Design for degradation, not perfection. Every layer should have a fallback.

Data-constrained
Data quality is your ceiling. No architecture, model, or prompt can compensate for upstream data problems.

Prompt Deploy explores how production AI systems succeed or fail in real environments.

I publish Prompt Deploy — a newsletter on production AI engineering judgment.

Selected articles:

• The AI Failure Cascade — How failures propagate through six predictable stages in AI systems, and why defense-in-depth is the only reliable strategy.
• Should We Use AI Here? — Four decision gates before writing any AI code — problem definition, error tolerance, data readiness, and cost at scale.
• Why I Think About Data Before Models — Why data quality is your ceiling — and a four-quadrant framework for evaluating source, quality, lineage, and privacy before touching the model.

More:
RAG vs Fine-tuning |
Build Discipline vs Run Discipline |
Build vs Buy: Vendor Strategy for AI Features

Short breakdowns on production AI engineering judgment.

• The first failure mode I'd look for in an AI feature
• When I Would Not Use RAG in a Decision Path
• When a Heuristic Beats a Model
• Why "It works in ChatGPT" isn't a production argument

More: https://youtube.com/@PromptDeploy

Princeton University — B.A. (Cum Laude), Senior Thesis Award Recipient

Industry experience: Allergan Aesthetics (AbbVie), GameStop, Zocdoc, StyleSeat — building enterprise payments, healthcare scheduling, consumer platforms, and Web3 systems. My experience shaped how I think about system design, on-call ownership, and long-term maintainability.

Consulting: Founder of Kappa Innovation LLC (2017–present) — full-stack engineering, AI/LLM integration architecture, and technical curriculum design.

Teaching: 500+ engineers trained live across General Assembly bootcamps, enterprise workshops (American Express, Walmart, Intuit, Northern Trust via PluralSight), and online courses. Teaching reinforced my bias toward clarity, fundamentals, and avoiding unnecessary complexity.

Certifications: Certified AI Engineer (AI Makerspace) | Agentic AI Nanodegree (Udacity)

Production-grade patterns for building reliable LLM systems with runnable code and cost benchmarks. Includes implementations for:

• graceful degradation
• semantic caching
• structured output validation
• token budgets
• multi-provider failover
• evaluation harnesses
• structured tracing

Structured design reviews examining how AI features behave in production environments. Each review analyzes:

• system context
• failure modes
• cost models
• deployment strategy

Example domains include B2B support automation, healthcare document summarization, and semantic commerce search.

An AI-powered design-to-code pipeline that transforms visual inputs (screenshots and Figma) into accessible, production-ready React components.

Key Engineering Pillars:

• Design System Integrity: Uses a multi-agent RAG architecture to ensure generated code strictly follows shadcn/ui patterns and local design system tokens.
• Structured Orchestration: Moves beyond "one-shot" generation by using specialized agents to handle visual analysis, architectural mapping, and accessibility validation.
• Operational Reliability: Built with the same discipline as a standard full-stack dependency, focusing on observability and predictable component output rather than just creative generation.

I'm looking for teams where production engineering rigor meets AI integration — companies building AI features into real products that care about reliability, cost, and trust as much as capability.

Get in touch:
kchia87@gmail.com
LinkedIn