Open to full-time & part-time roles · Remote (US)

I turn real-world workflows into AI agents that do the work.

I sit with the people doing the work, map it, and build the agents, integrations, evals, and guardrails that take it over. At BAM that means prior authorization, payer calls, and EHR/FHIR integration for specialty medical practices, under HIPAA, with a live pilot and code in production.

AI Solutions Architect & EngineerForward Deployed AIHealthcare AI

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01 Approach

Most AI never leaves the demo. I start where the work actually happens: on the clinic floor, on hold with payers, inside the EHR. I map it, then build agents that take it over, with evals, with guardrails, in production.

02 Proof

Numbers, not adjectives.

Workflow capture · 3 clinics

7,900

hours of consented clinic workflow footage captured and mined for automation

Each dot is 100 hours of footage

Voice agent · payer calls

~5×

lower modeled cost per payer call than commercial voice-agent platforms

Paid models stay idle through phone menus and hold: 54% of call time

Prior authorization · live pilot

56

prior-auth requests resolved in the first 13 days of a live clinic pilot, each with payer proof on file

8 approved · 48 confirmed not required · each square is one flagged request

Program leadership · since 2012

13+

years of global technical program leadership, from localization and IP operations to production AI

27-country rollouts · 9,000+ patent projects a year

03 Systems

Agents in production, end to end.

Each started with discovery alongside the people doing the work and ended in production code, evals, and guardrails. Details are generalized to protect clients and patients.

Keep scrolling

01BAM · 2026Live pilot

Multichannel prior-authorization agent

Routes every flagged case to the channel most likely to resolve it — clearinghouse e-filing, a payer-portal browser agent, an AI voice caller, published payer policy, or fax — under a deterministic state machine with an append-only evidence ledger.

  • 82% of flagged requests resolved in 13 days (56 of 68)
  • Payer rules-as-data, duplicate-filing guards, 360+ new tests
  • PHI only reaches BAA-covered, zero-retention models
PythonFastAPIAWS Bedrock AgentCorePlaywright / CDPAvaility X12 278
02BAM · 2026Live payer calls

Switchboard: a hold-aware voice agent

Payer calls are mostly waiting. A gatekeeper state machine handles dialing, phone menus, and hold without paid models, then wakes a realtime voice model the moment a human picks up — and verifies the outcome in code.

  • 54% of 17.5 recorded call-hours were hold time
  • ~5× lower modeled cost per call than commercial platforms
  • Blind two-agent eval lab with LLM-as-judge across 19 engines
PythonTelnyxDeepgramOpenAI RealtimeGemini LiveFly.io
03BAM · 202630-machine fleet

Workflow capture & mining

A signed, MDM-deployed capture agent on clinic workstations, and a pipeline that turns the footage into PHI-redacted process maps, task inventories, and role specs.

  • ~7,900 hours of consented footage, 3 clinics
  • Verified against click and keystroke logs (0.96 mean coverage)
  • Fed a ranked roadmap of 28 automation opportunities
PythonffmpegVertex AI · GeminiClaude
04BAM · 2026In production

EHR & clearinghouse integrations

ModMed’s certified HL7 FHIR R4 API via SMART Backend Services (JWT client assertions, JWKS), plus Availity eligibility (X12 270/271) and prior authorization (278) with a shared OAuth token cache.

  • ~13.6K lines merged to production
  • Tenant-to-practice binding prevents cross-tenant PHI access
HL7 FHIR R4SMARTX12 EDIOAuth 2.0AWS Lambda
05RepeatMD · 2025–26Shipped

CX Agent & Platform Agent

A support copilot inside the admin console (RAG, live SQL, tool calling), and a 13-model pipeline that turns messy client files into structured app-build specs.

  • 13 models (8 generation, 5 vision) via OpenRouter
  • 8-criterion QA scoring, JSON repair, model fallback
  • App-build specs for ~15 client practices
Next.jsTypeScriptPythonpgvectorOpenRouter

04 Under the hood

Watch an agent work.

Every system I ship is observable: each model call, tool call, and guardrail is a span you can inspect. These are illustrative replays with synthetic data, modeled on the systems above.

Inspect a span

—

Duration—
Model calls—
Tool calls—
Guardrails passed—
Model cost—

modeltoolguardrailcode / ioidle (no model)

05 Capabilities

A map of the evidence.

Every skill connects to the roles where I used it. Hover to trace the connections, click a node to read the résumé lines behind it, drag to rearrange.

Evidence

Pick a skill or a role

    AI agents & LLMs

    Evals, guardrails & LLMOps

    Healthcare interoperability

    Engineering & cloud

    Delivery & enablement

    Global operations

    Résumé summary and full skills list

    AI Solutions Architect and hands-on engineer who takes agentic AI from technical discovery to production in regulated, customer-facing settings. At BAM, a healthcare revenue cycle management (RCM) startup, designs and ships AI agents (voice, browser, and large language model (LLM) pipelines), EHR/FHIR and clearinghouse integrations, evals, and HIPAA guardrails, including a prior-authorization agent now live at a specialty practice. Brings 13+ years of global technical program management to stakeholder alignment, adoption, and measurable ROI.

    • AI Agents & LLMs: generative AI, AI agents, agentic workflows, multi-agent orchestration, tool use / function calling, Retrieval-Augmented Generation (RAG), vector search (pgvector), context engineering, prompt engineering, structured outputs, Model Context Protocol (MCP), browser / computer-use agents, voice AI (STT, TTS, realtime), human-in-the-loop (HITL)
    • Evals & LLMOps: evaluation frameworks (evals), LLM-as-judge, A/B testing, regression gates, guardrails, observability, model routing and fallback, prompt caching, cost and latency optimization, responsible AI
    • Models & Platforms: Anthropic Claude (Claude Code), OpenAI (GPT, Realtime API), Google Gemini (Vertex AI), AWS Bedrock AgentCore, OpenRouter, Ollama, LangGraph, LangChain, Deepgram, ElevenLabs, Telnyx
    • Engineering & Cloud: Python, TypeScript, SQL, FastAPI, Next.js / React, Node.js, PostgreSQL, REST APIs, OAuth 2.0 / JWT, AWS (Lambda, SQS, S3, CDK), GCP, Azure, Docker, Kubernetes, GitHub Actions CI/CD, Playwright, pytest
    • Healthcare: HIPAA, PHI, Business Associate Agreements (BAA), EHR/EMR integration (ModMed), HL7 FHIR R4, SMART Backend Services, X12 270/271 eligibility verification, X12 278 prior authorization, clearinghouses (Availity), payer portals, revenue cycle management (RCM)
    • Delivery & Leadership: solution architecture, technical discovery, workflow mapping, POCs and MVPs, pilots and go-live, stakeholder management, executive communication, change management, AI adoption and enablement, playbooks and SOPs, roadmaps and OKRs, technical program management, Agile / Scrum, RFP / RFI, vendor management

    06 Experience

    From global operations to agentic AI.

    The same content as the PDF résumé, line for line: 13+ years of operating discipline (vendor networks, quality programs, regulated delivery), now pointed at production AI.

    Dec 2025 – PresentRemoteCurrent role

    AI Solutions Architect & Engineer · BAM

    AI-native revenue cycle management (RCM) platform automating eligibility, billing, and prior authorization for specialty practices.

    • Architected and built BAM’s multichannel prior-authorization (PA) agent, routing each case across clearinghouse e-filing (Availity X12 278), a payer-portal browser agent (AWS Bedrock AgentCore), AI voice calls, and fax under a deterministic state machine with an append-only evidence ledger (360+ new tests).
    • Led a live, BAA-covered pilot with a Texas ENT practice through go-live: resolved 82% of flagged prior-auth requests (56 of 68) in 13 days across 110+ payer calls to 9 payers.
    • Built Switchboard, a hold-aware AI voice agent for payer calls (Python, FastAPI, Telnyx, Deepgram, OpenAI Realtime, Gemini Live): paid models stay idle through IVR and hold (54% of call time), for ~5x lower modeled cost per call than commercial voice-agent platforms.
    • Designed the evaluation framework for voice and browser agents: a blind two-agent call lab with LLM-as-judge, A/B tests across 19 speech and LLM engines, and cost, latency, accuracy, and AI-disclosure regression gates.
    • Integrated ModMed’s certified HL7 FHIR R4 API (SMART Backend Services, JWT client assertions, JWKS) and hardened Availity eligibility and PA integrations (X12 270/271, 278), merging ~13.6K lines to production (FastAPI, Next.js, PostgreSQL, AWS Lambda).
    • Defined BAM’s PHI guardrails for AI: PHI reaches only BAA-covered, zero-retention models (OpenAI, Vertex AI) while other models see redacted data; PHI-free logs and URLs; AES-256-GCM storage; a secrets vault with automated TOTP sign-in.
    • Shipped a signed, MDM-deployed capture agent to 30 clinic workstations (56 releases), recording ~7,900 hours of consented workflow footage, and built the pipeline that mines it into PHI-redacted process maps and role specs (Gemini 2.5 Pro on Vertex AI, Claude, verifier agents).
    • Translated observed clinic workflows into a ranked automation roadmap of 28 evidence-linked issues with minutes-saved estimates that set BAM’s build priorities.
    • Prototyped BAM’s first desktop product in 10 days (Electron, React, Gemini) and shipped internal agents: a Slack footage “librarian,” a PR agent with Claude-critic quality gates, and a meeting-to-PR coding agent (Claude Code).
    PythonTypeScriptFastAPINext.jsPostgreSQLAWSVertex AIClaudeOpenAI RealtimeHL7 FHIRX12
    Nov 2025 – Mar 2026Remote

    Head of AI Enablement (Consulting Engagement) · RepeatMD

    Membership, loyalty, and commerce platform for medical aesthetics practices.

    • Owned the AI enablement roadmap for CX and platform operations: ran workflow discovery with support and onboarding teams, then shipped LLM applications, adoption plans, SOPs, and human-in-the-loop review patterns.
    • Built the RepeatMD CX Agent (Next.js, TypeScript, OpenRouter tool calling): RAG over help-center and Notion content (pgvector) plus live Metabase and SQL data, embedded in the admin console as a Chrome side panel.
    • Architected Platform Agent, a multi-model Python pipeline turning unstructured client inputs (PDFs, spreadsheets, images, Gong call transcripts) into structured app-build specs for ~15 client practices.
    • Orchestrated 13 models (8 generation, 5 vision) via OpenRouter with routing and fallback, prompt caching, adaptive chunking, JSON repair, and 8-criterion QA scoring; added a 24-worker Gemini tax-code classifier (US and Canada).
    Next.jsTypeScriptPythonpgvectorOpenRouterMetabaseChrome extension
    2023 – 2025

    Director of Operations · GreyEye

    • Led the AI operations roadmap for local inference, workflow automation, and customer-facing prototypes; ran discovery and rapid POCs that lifted operational efficiency 40% and profitability 15% in six months.
    • Deployed local LLMs (Python, Ollama, GPU) that cut inference latency 25%; engineered multi-model consensus across GPT-4, Claude, Gemini, Grok, and o-series models, improving output accuracy 30%.
    • Built CI/CD for AI workflows (40% faster deployments, 99% production reliability) and migrated workloads to AWS and GCP GPU environments, cutting infrastructure costs 20%.
    PythonOllamaGPUAWSGCPCI/CD
    2018 – 2023

    Senior Program Manager · SimulTrans

    • Owned the cross-functional delivery roadmap for a global quick-service restaurant (QSR) enterprise across 27 countries: $100K+ first-year revenue at a 53% profit margin.
    • Integrated GPT-4 into production translation workflows in 2023, cutting turnaround 30% with human review controls; automated quoting, vendor management, QA, and invoicing in XTRF (25% faster turnaround).
    • Built analytics dashboards and macros that reclaimed 35% of management time and lifted CSAT 15%; restored a critical client relationship, adding $500K+ in revenue (2019–2020).
    GPT-4XTRFAutomationAnalytics
    2016 – 2018

    Program Manager, Talent & Quality · Welocalize

    • Designed live linguistic quality assurance (LQA) processes that raised client satisfaction scores to 95%; automated translator workload distribution (20% faster delivery, 15% less coordinator workload); introduced AI-assisted machine translation post-editing, cutting costs 10%.
    LQAMT post-editing
    2013 – 2016

    Program Manager, IP Translations · MultiLing

    • Managed 9,000+ patent translation projects a year for Fortune 100 clients; built IP translation and validation systems that sped processing 90%, raised profitability 36%, and cut errors 25%.
    Patent operationsProcess automation
    2012 – 2013

    Senior Localization Project Manager · Moravia

    • Managed global app, web, and technical localization programs at 45%+ profit margins; QA protocols reduced rework 30%.
    Global deliveryQA systems

    Education

    • B.A., LinguisticsBrigham Young University · 2008 · Minor: Language & Computers
    • B.A., PortugueseBrigham Young University · 2008 · 24-month residency, Rio de Janeiro, Brazil
    • LanguagesEnglish (native) · Portuguese (fluent)

    Awards

    • Annual Automation Competition, 1st PlaceMultiLing · 2013
    • Employee of the YearMultiLing · 2014
    • Team of the YearMultiLing · 2015

    Certifications & publications

    • Foreign Patent Filing CertificateIP Paralegal Institute · 2013
    • Patent Paralegal CertificateIP Legal Ed. · 2013
    • “Translation Pitfalls Mirror the Asian IP Boom”Intellectual Property Magazine · 2013
    • International Guide to Patent Application Translation & FilingMultiLing · 2016
    • 40 Lessons in Portuguese Grammar2008

    07 Ask the résumé

    Grounded answers, with receipts.

    Ask anything about my background. Claude answers using only the passages on this page, cites every claim with a numbered link to its source line, and says so when the page doesn’t cover something.

    Your question goes through a small Cloudflare Worker to Claude, and this site doesn’t log or store it. If the model is unavailable, an in-browser BM25 search answers instead. It’s the retrieve-then-cite pattern I build into production agents.

    08 Colophon

    How this site was built.

    The same way I build agents: one source of truth, specialist agents, automated checks, and a human approving what ships.

    1. 01

      Research agents

      Mined my own repositories for evidence, and 255 live job postings for the language hiring teams use.

    2. 02

      One source of truth

      The résumé is a single file. The PDF, the Word version, and this page are generated from it, so they can’t disagree.

    3. 03

      Translator agents

      Nine languages, every line fingerprinted: if the English changes, an outdated translation falls back to English instead of contradicting it.

    4. 04

      Automated QA

      Playwright for layout and interactions, axe-core for accessibility, and PDF text-layer checks so résumé parsers read every word.

    5. 05

      Human in the loop

      Every claim is traced to evidence, and nothing ships until I approve it.

    Build receipttyleryoung.org

    Research agent runs
    3
    Job postings analyzed
    255
    Translator agent runs
    6
    Design-critique agent runs
    1
    Languages
    10
    Strings fingerprinted per language
    285
    Accessibility violations (axe-core)
    0
    JavaScript, gzipped
    94 KB
    Particles on screen, live
    16,384
    Frame rate, live
    —
    Frameworks
    0

    Reviewed and approved by a human

    WebGL · custom GLSLGSAP ScrollTriggerLenisBM25 · vanilla JS10 languagesNo framework

    09 Contact

    Hiring for agentic AI, deployment, or enablement? Let’s talk.

    Forward deployed, solutions architecture, applied AI, and AI enablement roles. Based in Tucson, Arizona, working remotely across US time zones.

    Where · Tucson, AZ · Remote (US) Languages · English · Portuguese Available · Full-time & part-time roles