Chris L.

AI Developer & Systems Architect | OpenAI, RAG | Secure AI Platforms

I design and deliver AI-powered knowledge systems for organizations where accuracy, security, and architectural discipline matter. Not demo chatbots. Not hype prototypes. Not “AI wrappers.” I build controlled, retrieval-augmented intelligence systems that operate reliably under real-world constraints — especially in document-intensive and regulated environments. With 30+ years in software engineering, I specialize in: Retrieval-Augmented Generation (RAG) systems Secure document ingestion and indexing pipelines Role-based access control (RBAC) for sensitive content Audit logging and observability for AI systems Deterministic AI workflows with controlled grounding Production-ready deployments under compressed timelines If you’re building an AI assistant that must respect permissions, cite sources, survive scrutiny, and scale responsibly — that’s my lane. 🔷 What I Build 🔹 Secure AI Knowledge Portals Matter-aware or domain-aware document ingestion Chunking strategies for contracts, briefs, manuals, technical documents Vector database architecture (ChromaDB, Pinecone, Qdrant, etc.) Permission-aware retrieval at query time Context fusion layers that constrain model output to trusted knowledge Structured citation injection into responses Comprehensive audit logging (inputs, retrieved docs, model outputs) Used in: Legal environments Advisory and research systems High-trust internal knowledge platforms 🔹 Expert Advisor & Intelligence Systems Domain-specific AI copilots Industry knowledge assistants Contract and policy analysis tools Decision-support dashboards Multi-thread persistent conversations Prompt orchestration tuned for nuance and accuracy These systems are designed to: Reduce hallucination risk Ground responses in owned documents Preserve explainability Support executive-level decision making 🔹 Structured Ingestion & Knowledge Engineering PDF/DOCX processing pipelines OCR integration where required Schema-aware metadata tagging Embedding strategies tuned for long-form documents Hybrid retrieval (semantic + keyword) Evaluation workflows to test answer quality I design ingestion pipelines to be repeatable, scalable, and auditable — not ad hoc. 🔹 AI System Observability & Guardrails Logging of retrieval decisions Monitoring of model drift and failure cases Clear separation between AI reasoning and source authority Retry and fallback logic Administrative dashboards for oversight When AI is used in professional settings, oversight matters. I build for that from day one. 🔷 Demonstrated Experience Built and deployed a RAG-based knowledge assistant embedding and indexing 40,000+ documents, supporting persistent multi-thread research workflows. Designed context fusion layers that inject top-ranked documents directly into model prompts to constrain output. Implemented structured citation systems tied to vector retrieval. Delivered complex AI systems under aggressive timelines (multi-week builds). Built large-scale ingestion pipelines without prior framework dependencies. Extensive experience designing backend-first architectures that minimize risk. I’ve built production systems in: Legal-adjacent environments Healthcare-related platforms Financial and advisory tools Large-scale content knowledge systems Enterprise dashboards and monitoring platforms 🔷 Technical Stack Backend: Python (FastAPI), Node.js REST APIs JWT authentication Role-based access control systems AI: OpenAI / Anthropic APIs RAG architecture Prompt orchestration Embedding pipelines Hybrid retrieval strategies Vector & Storage: ChromaDB Pinecone Qdrant PostgreSQL MongoDB Infrastructure: Docker / Compose AWS (EC2, S3, RDS) Cloud deployment patterns Secure web application deployment Frontend: React Clean, minimal UI for AI interfaces Admin dashboards Streaming response UX (when required) 🔷 How I Work Architecture-first execution Scope discipline under compressed timelines Clear execution order (foundation → ingestion → retrieval → orchestration → admin) Risk surfaced early No hidden complexity No “we’ll fix it later” AI shortcuts If something threatens reliability or compliance, I flag it immediately. 🔷 Engagement Style I’m best suited for: Fixed-price MVP builds ($15k–$40k) High-trust internal platforms Advisory AI systems Milestone-based delivery Short, intense build windows Architecture stabilization before scale I am comfortable committing full-time during compressed build windows when required. 🔷 Background 30+ years professional engineering experience Author of: High Performance Single Page Web Applications Progressive Web App Development by Example Former Microsoft MVP (14 awards) Speaker at developer and performance conferences I bring senior-level judgment to AI systems that must operate beyond proof-of-concept. 🔷 If You’re Building… A secure legal AI portal An industry-specific knowledge advisor A document-intensive