Applied AI Engineer

Production AI Systems, End to End

I build workflow APIs, multi-agent systems, real-time voice pipelines, and Python backends for production AI use cases. Public proof includes live demos, shipped tooling, open source work, and 3,500+ automated tests across production repos.

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Workflow APIs Multi-Agent Systems Real-Time Voice AI PyPI Published 3,500+ Tests Open Source Contributor

What I Build

Production AI infrastructure — from workflow APIs to multi-agent systems

Workflow APIs and Backend Systems

FastAPI services, async workers, Server-Sent Events, integrations, and production-friendly interfaces for multi-step AI workflows.

Agentic AI / Multi-Agent Systems

Multi-agent orchestration, layered caching, model routing, conditional workflows, tool use, and systems that stay understandable to hiring managers.

Real-Time and Evaluation Systems

WebSocket streaming, voice activity detection, STT/TTS, barge-in handling, eval-driven delivery, and test-heavy engineering to keep AI systems reliable.

Projects

Best first-click proof for hiring teams: EnterpriseHub, AI Workflow API, TechNova Voice Bot, and Multi-Agent Demo. The sections below include older supporting projects as well.

Production · Live Client

Jorge Real Estate AI

3 Claude-powered SMS bots handling lead qualification for a real estate firm. 500+ leads processed, under 500ms response time, bilingual EN/ES, zero downtime over 3-month production run.

Capabilities

  • Lead Intake, Buyer, and Seller qualification bots
  • Tiered model routing (Haiku/Sonnet/Opus)
  • GoHighLevel CRM integration via webhooks
  • Bilingual English/Spanish with no quality degradation

Stack

  • Python, FastAPI, Redis, PostgreSQL
  • Claude API (tool_use, streaming, multi-turn)
  • GoHighLevel API, Twilio SMS
  • 1,700+ tests · Render deployment
Production RAG · Live Demo

DocExtract AI

Async document processing with hybrid retrieval, citation-aware answers, and agentic ReAct reasoning. 95.5% F1 on a 28-case CI-replayed golden baseline; eval corpus 72 cases (51 golden + 21 adversarial, incl. prompt injection).

Capabilities

  • Hybrid retrieval: BM25 + cosine + RRF
  • Semantic caching (88% hit rate)
  • Circuit breaker model fallback
  • RAGAS evaluation + LLM-as-judge CI gate

Stack

  • FastAPI, ARQ, pgvector, Claude API
  • Sentence Transformers, Streamlit
  • Reference deployment configs, eval CI, live demo
  • 1,280 tests · 81% coverage
Live Demo → GitHub →
Multi-Agent Orchestration

EnterpriseHub

Domain-specific agent mesh with 3-tier cache achieving 88% aggregate hit rate. 8 agent capabilities, circuit-breaker failover, per-agent model routing, OWASP-hardened security, and OpenTelemetry instrumentation.

Capabilities

  • Lead Intake, Buyer, Seller agent mesh
  • L1 memory, L2 Redis, L3 PostgreSQL cache
  • Per-agent model routing (Haiku/Sonnet/Opus)
  • Ed25519 webhook verification, Redis rate limiting

Stack

  • FastAPI, PostgreSQL, Redis, LangGraph
  • Claude API, Prometheus, Grafana
  • OpenTelemetry, 9-panel dashboard configs
  • 22-agent platform
GitHub →
PyPI Package · Published

mcp-server-toolkit

9 pre-built MCP servers with A2A adapter, auto-caching, rate limiting, auth middleware. MCPTestClient for testing without live API keys. Reduces LLM tool integration from days to a single import.

9 MCP servers · A2A adapter · 600 tests · 82.87% coverage
GitHub →

Open Source Contributions

LiteLLM · 27K+ stars

Typed Exception Mapping for Router Fallback

PR #24551 -- Surfaces AuthenticationError, RateLimitError, and NotFoundError distinctly through the Router fallback chain instead of swallowing as generic Exception. Enables callers to implement appropriate recovery strategies per error type.

Also: open PRs in FastAPI (80K+ stars, #15217) and pgvector-python (#151)

Selected AI Certifications

IBM Generative AI Engineering 144 hours
DeepLearning.AI Deep Learning Specialization 120 hours
Microsoft AI & ML Engineering 75 hours
Duke University LLMOps Specialization 48 hours
IBM RAG and Agentic AI 24 hours
Google Cloud Generative AI Leader 25 hours
Claude Code in Action — Anthropic 3 hours

Selected from 21 certifications totaling 1,831 hours across IBM, DeepLearning.AI, Microsoft, Duke, Google, Vanderbilt, and Anthropic.

Open to Applied AI Engineer Roles — Remote

Targeting teams building workflow APIs, agent systems, real-time AI applications, and developer tooling. Best fit: Applied AI Engineer, AI Engineer, AI Backend Engineer, and LLM Engineer roles.

US-based (Cathedral City, CA) · Canadian citizen, no sponsorship required

caymanroden@gmail.com