I build production-grade full-stack systems and AI-driven applications for financial services — spanning data-intensive React and TypeScript interfaces, resilient backend services, and production data workflows where performance, correctness, and usability all matter.
My background spans equities trading and banking platforms at firms including Nomura and JPMorgan, where I built real-time blotters, dashboards, and trade booking systems under continuous data load. I understand what financial UIs need to do and why — order lifecycle, streaming state, audit trails, and the domain logic underneath.
Recently I've extended into AI integration — building RAG pipelines, LLM-powered workflows, and AI-assisted interfaces that bring intelligent features into production systems. I apply the same engineering discipline to AI that I bring to full-stack engineering — clear boundaries, validation, and observable behavior.
I actively use AI-assisted development workflows, applying the same engineering rigor to AI coding tools as I do to production systems. Structured prompting, validating generated code, and integrating AI output with proper testing and review — accelerating delivery without sacrificing quality or maintainability.
Core expertise
React · TypeScript · WebSockets · Real-time data · Financial systems · RAG pipelines · LLM integration · Python · FastAPI · PostgreSQL · AWS · GCP
Featured Systems
FlowDesk
GitHubReal-time order management system for trading teams — live blotter, P&L tracking, full audit trail, and AI-powered summarization and NLP filtering.
Architecture: Event-driven • WebSocket streaming • Component-based state management
Production Use Cases: Equities: Order lifecycle management, real-time P&L, risk exposure tracking • AI: Natural language order filtering, session summarization, EOD report generation • Audit: Immutable field-level change trail with actor and timestamp
Ask my Docs (RAG)
GitHubRetrieval-augmented Q&A over documents and data — ingestion and chunking, embedding pipelines, vector retrieval with reranking, grounded answers with citations, and evaluation hooks for production rollout.
Architecture: Event-driven ingestion • Embedding + vector index • Retriever + reranker • LLM orchestration • Observability and eval
Production Use Cases: Knowledge bases: Internal docs and policy Q&A • Support: Ticket and runbook search with sources • Compliance: Cited answers and audit-friendly retrieval logs
Data Mind
GitHubNatural-language analytics and exploration layer — connect sources, ask questions in plain language, surface metrics and relationships, and trace results back to underlying tables and definitions.
Architecture: API-backed services • Structured + semantic query paths • Metric and entity models • Lineage and audit-friendly responses
Production Use Cases: Analytics: Ad hoc NL queries over approved datasets • Self-serve: Consistent metrics and definitions for teams • Governance: Actor, timestamp, and source attribution on answers
Other Systems
AI Governance Platform
Enterprise AI control plane with pluggable policy enforcement, dual-checkpoint validation, full audit trails, and human-in-the-loop workflows — built for regulated environments that need auditability and controlled execution.
Agentic Enterprise Agent
Constrained planner–executor for enterprise: the LLM proposes a plan (tool calls); validation and execution are deterministic and auditable. Clear audit trail, no black-box runs, governance plug-in before any tool executes.
Technical Work and Insights
ADRs
├─ Policy Outcome Model Design
Framework for modeling and evaluating policy outcomes in AI governance systems.
├─ Sync vs Async Communication Patterns
Decision framework for choosing synchronous vs asynchronous communication in distributed AI systems.
└─ Modular Monolith to Microservices Strategy
Migration approach for evolving monolithic AI platforms into distributed microservices architecture.
Technical Writing (coming soon)
└─ How the Governance Platform scales 10x → 1000x
Documenting the evolution and scaling challenges of AI governance platform.
Case Studies
├─ NLP-to-SQL Interface for Enterprise Databases
Enables non-technical users to query enterprise databases in plain English with validation and visualization, reducing analysis time from hours to minutes.
└─ RAG-Powered Document Intelligence System
Production RAG system providing instant, context-aware access to enterprise documentation with semantic search and source attribution.
Contact
Memphis, TN | Previously New York, NY