AI interprets.
Extracts structure and semantic intent from noisy payloads without raw write authority.
AI Engineering
Built for work beyond the model call.
AI agents, durable automation, backend systems, data boundaries, human approval, failure recovery, and the product around them.
PRODUCTION STACKFastAPI · PostgreSQL · LangGraphSAFETY FIRSTOutbox · Idempotency · RBACVERIFIABLE99.2% Eval · 100% Audit trail
01 / Selected engineering proof
RevenueOS production architecture & core stack.
Autonomous systems fail without deterministic constraints. Every system is built on battle-tested backend primitives.
01LangGraph
Stateful agent graphs and durable multi-step checkpointing
02FastAPI
High-throughput asynchronous service boundary
03PostgreSQL
ACID transactional state and audit records
04Celery / Redis
Distributed background job queues and rate limits
05Transactional Outbox
Idempotent side-effect guarantees
06RBAC
Role-based permission scopes and access tokens
07Human Approval
Explicit human-in-the-loop decision checkpoints
08Idempotency Keys
Zero duplicated writes on network retries
09Integration Tests
Deterministic end-to-end evaluation runs
02 / Architectural philosophy
How I engineer AI systems.
The rules governing intelligence, deterministic software, and human authority.
Code constrains.
Enforces hard deterministic rules, schema contracts, and mathematical validation.
Humans authorize risk.
Consequential financial, data, or destructive actions require explicit approval.
State persists.
Durable workflows survive server restarts with database-backed checkpoints.
Failures recover.
Graceful rollbacks, dead-letter retries, and bounded escalation paths.
Logs explain.
Every execution produces an immutable, replayable audit trail of decisions.
03 / Background
Factual experience.
AI Systems EngineerProduction Systems & Client Engagements
2024 — PresentArchitecting AI-native operating systems, transactional outbox pipelines, and autonomous agent workflows with human-in-the-loop safety boundaries.
Full-Stack & Backend EngineerProduction Web Applications
2022 — 2024Built distributed backend services, real-time data sync engines, and API integrations across CRM, ERP, and payment systems.
04 / Capabilities
Technical capabilities.
AI Engineering
LangGraph, agent graphs, deterministic tool calling, evals, guardrails, context windows, semantic routing, RAG.
Backend Engineering
FastAPI, Node.js, Python, PostgreSQL, Redis, Celery, outbox pattern, distributed task execution, schema contracts.
Product Engineering
Next.js (App Router), TypeScript, Tailwind CSS, real-time interfaces, multi-tenant state, role-based access control.
Systems Design
Reliability engineering, idempotency, failure recovery, audit logs, observability, data privacy, security boundaries.
05 / Flagship projects
Three production-grade operating systems.
FLAGSHIP SYSTEM
RevenueOS
Autonomous inbound pipeline with multi-source evidence synthesis, LangGraph durable worker execution, and human approval gates.
View deep-dive architecture ↗FINANCIAL PIPELINE
FinanceOps
Autonomous reconciliation engine with strict deterministic math checks, 3-way invoice matching, and ERP write locks.
View reconciliation system ↗OPERATIONAL SUPPORT
SupportOS
Deterministic ticket routing with bounded autonomous execution, dynamic customer context retrieval, and safety escalations.
View support system ↗Hiring for AI engineering?
If the role involves AI agents, backend systems, or automation, let's talk.
Schedule a focused 30-minute technical conversation. We'll discuss engineering scope, system architecture, and team fit.
Direct technical conversation.A focused 30-minute discussion on role scope, backend & agent architecture, and team fit. Direct calendar confirmation with zero unnecessary back-and-forth.
✓ Direct calendar booking · 30-minute conversation