I don't wait for a team. I don't wait for a playbook. I find a way — and I build what shouldn't exist yet.
I build the data foundation from scratch. I put autonomous AI on top of it. I ship it to production. Then I make it pay for itself.
Customer Problems → Data Infrastructure → Autonomous AI → Real Outcomes.
That's not a methodology. That's how I'm wired.
Early in my career, I was on a trading desk when a coworker made an error — 1 error among thousands of trades entered that day. It wasn't my problem. I pulled up a chair anyway, ordered food in, and at 4am we found it. I was back at my desk by 5 am.
That's not a story about heroics.
That's just how I operate.
Today, I architect enterprise-scale AI systems that solve real customer problems — tracing every issue from the customer conversation down to the data infrastructure underneath, then engineering solutions that actually ship.
I've raced across the Atlantic with a crew of four. I've built petabyte-scale data lakes from scratch. I've deployed autonomous agent systems with 40+ tools running without human direction.
None of those required permission. All of them required showing up.
Not "I've worked with data pipelines." I've built them from scratch.
- Petabyte-scale data lakes, marts, and full Bronze → Silver → Gold medallion architecture on Apache Spark — designed, built, and shipped
- Streaming + batch systems engineered for predictable spend — intelligent partitioning, auto-scaling, FinOps-aware from day one
- Multi-cloud data architectures (AWS + GCP + Azure) with unified governance, lineage, and cost attribution
- The insight nobody talks about: Governance isn't a compliance problem. It's a leadership problem. The moment "customer" means the same thing in finance, sales, and product — everything changes.
Not demos. Not pilots. Production.
- 40+ tool autonomous agent systems operating in live financial markets without human direction — real decisions, real consequences, real time
- Multi-agent orchestration: Swarms, Claude Agent Teams, LangGraph, AgentCore, Strands — I don't use one framework, I compose them
- MCP server architecture — built and deployed from Python
- RAG pipelines processing 10,000+ documents and 70+ hours of audio — driving measurable business outcomes
- Voice AI pipelines: ElevenLabs + Twilio + Supabase — concept to production
- The line that matters: Agents should be autonomous. They should never be ungoverned.
Not certified. Fluent.
- AWS: Lambda, S3, CloudFront, SQS, Route 53, App Runner, API Gateway, Bedrock, SageMaker
- GCP: BigQuery, TPU access, specialized ML workloads
- FinOps built in from day zero — not bolted on after the bill arrives
Not cost-cutting. Capital reallocation.
- $8.8M waste identified in a $200M SaaS acquisition target — recommended $15M valuation haircut, $32.7M 5-year NPV
- $5.4M saved over 24 months (59% reduction) — zero SLA degradation, 100% reinvested in customer experience
- AI/ML workload optimization: $1M+ annual savings while maintaining cutting-edge model performance
- M&A infrastructure due diligence frameworks built for PE firms, CTOs, and CFOs who need answers before the wire transfer clears
Not notebooks. Production.
- Linux-native, Kubernetes-first MLOps platforms using Terraform IaC
- Model serving, inference optimization, hybrid batch/streaming workflows
- Spot instances for training. Reserved capacity for inference. Always.
- Observability, SLOs, chaos engineering, rollback — table stakes not afterthoughts
Production-ready algorithmic trading workflows — backtesting, real-time data pipelines, Random Forest + XGBoost models integrated into live trading bots. FinOps-aware quant systems that scale profitably. 6-project series in progress.
Hybrid quantum-classical algorithms for portfolio optimization, anomaly detection, and multi-cloud resource allocation. Not science fiction — working code.
- Quantum teleportation protocol → github.com/TAM-DS/Quant11
- Hybrid Quantum-Classical Classifier — 100% test accuracy on make_moons → Quantum-Hybrid-Moons-Classifier
- VQE Ground-State Energy — chemical accuracy (~-1.852 Hartree, error <1 mHa) → Quantum-Chemistry-VQE-H2
Physics-meets-economics framework modeling the shift to space-based AI. Free radiative cooling. Solar efficiency. Tipping point at <$50/kg launch. Predicting 25–40% of exascale AI training in orbit by 2034–2037. Full series → Orbital-AI-Security-Analysis-Series
Most people are optimizing for today's cloud. I'm modeling where compute lives in 2035.
Early-year contributions were lighter while I provided full-time home hospice care for my mother during her final months with brain cancer.
It was the hardest and most meaningful work I've ever done.
She taught me that showing up when it's hard — especially when nobody asked you to — is the only thing that actually matters.
That's the same instinct that drives everything I build.
I'm back to full capacity now. Recent activity shows it.
| What | Impact |
|---|---|
| Autonomous agent tools running in live markets | 40+ |
| Data infrastructure scale | Petabyte |
| Cloud waste identified in single M&A target | $8.8M |
| FinOps savings over 24 months | $5.4M |
| NPV modeled on post-acquisition value creation | $32.7M |
| RAG pipeline documents processed | 10,000+ |
| Audio processed through production pipelines | 70+ hours |
| Clouds I architect across simultaneously | 3 |
| Times I've waited for someone else to solve it | 0 |
Cloud & Native FinOps
Languages & Data
ML Frameworks & MLOps
Infra & DevOps
FinOps & Cost Optimization (2026 essentials for AI/ML-heavy workloads)
Observability & Monitoring (critical for tying cost to performance in production FinOps)
If you're building something that matters and need someone who owns the whole problem — I'm your person.
💼 LinkedIn 🐦 X 📧 Email 📊 Tableau Portfolio
📍 Greater San Antonio | Austin Metro — New Braunfels, TX
I pulled up a chair at 4am when nobody asked me to. I'm still here.