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.
| 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.