Experience

Production AI workflow infrastructure: agent execution, human review, evaluation, and orchestration.

• Agent Execution Harness: Architected the core runtime — turn and message lifecycle, idempotent claim semantics, and replay-safe Temporal orchestration. Every agent capability the platform ships (HITL, policies, evals, generative UI) runs on it.
• Live Evaluation Dashboard: Built production conversation-quality scoring that surfaces degradation early enough to intervene before customers feel it.
• Human-in-the-Loop Interventions: Shipped the escalation system end to end — a signal-driven resolve state machine with optimistic locking and tenant isolation, a 3-action resolution model, and the Temporal signal contract wiring escalations into live agent workflows.
• Generative UI Architecture: Designed a registry of approved components the harness selects from instead of generating markup each turn, cutting dashboard prompts from roughly 15k tokens to 700–2k; a JSON-Patch delta protocol saved 90% on multi-turn follow-up edits.
• Policy Engine & Multi-Model Inference: Built agent guardrails with policy-version pinning and full audit logging, and integrated AWS Bedrock behind one selection interface, cutting latency and cost through provider routing and prompt caching.

Advising on technical direction, architecture, and client delivery oversight for the studio I founded.

• Technical Direction: Advise on architecture, technology choices, and engineering standards for the studio I founded.
• Delivery Oversight: Review client engagements and solution designs without day-to-day execution ownership.

Servicing platform modernization at a consumer fintech serving millions of users, during the move from a Rails monolith to Node services, React frontends, and a GraphQL gateway.

• Full-Stack Delivery: Shipped production changes across Rails, Node services, React, Next.js, and GraphQL APIs throughout a phased monolith-to-microservices migration.
• Internal Tooling: Built GraphQL-driven admin and support tooling giving operations teams direct visibility into servicing workflows, and resolved gateway schema and data-consistency failures introduced by splitting Rails-owned data across independent services.
• Ephemeral Environments: Shipped per-PR environments to production, closing the staging/production parity gap that had been a recurring source of regressions.
• Regression Coverage: Partnered with platform and QA to extend automated regression coverage across the new Node services, reducing cutover risk during the migration.

Founded and led an AI consulting and product studio, run in parallel with full-time roles: agents, computer vision systems, analytics platforms, and automation infrastructure delivered to outside clients.

• Team & Technical Direction: Directed a 7-engineer team across client discovery, solution architecture, delivery, and engineering standards.
• Production AI Agents: Architected agents across logistics, customer support, operational workflows, and computer vision analysis, built on structured outputs, retrieval, model-backed classification, and human review loops rather than raw prompt calls.
• Foreign-Exchange Automation: Delivered end-to-end software and automation for a foreign-exchange house covering operational workflows, back-office processing, and third-party integrations.
• Analytics & Automation Platforms: Designed platforms spanning data ingestion, dashboards, paid-traffic systems, and business reporting.
• Revenue Impact: Delivered systems tied to roughly $2.8M USD in client revenue over 8 months.

Cloud-native products, data systems, and architecture consulting across fintech, education, content, and supply-chain clients.

• Cloud Infrastructure & Performance: Built production systems on AWS (ECS, RDS, ElastiCache, S3, CloudFront, ALB) with Cloudflare and Redis for low-latency fintech workloads serving millions of users.
• Microservices & High-Availability: Designed microservices and APIs for high-traffic clients; connection pooling and regional caching cut latency, then right-sizing compute brought infrastructure spend down.
• Event-Driven Data Pipelines: Architected SQS and async-worker pipelines for compliance-grade fintech operations.
• Key Clients: GS1, ANBIMA, YOU, Engagx, Evolucional, Panorama.

Built business systems, internal tools, and modernization work for operational software.

• Legacy System Modernization: Modernized legacy applications and improved stability for critical workflows.
• Cloud-Ready Architecture: Designed cloud-ready services with clearer performance, maintenance, and cost boundaries.

Built data extraction, API, and back-office systems for education operations.

• Data Extraction & Processing: Built Python scrapers that processed 5,000+ records per day.
• API Development: Developed REST APIs for internal data workflows.