Strategist with Technologist Soul + Consultant Polish | Austin, TX

I architect and deliver enterprise-scale AI transformations that bridge bold strategic vision with ruthless production reality — turning AI ambition into sustainable, cost-optimized, customer-obsessed systems that keep delivering value at 3 a.m. when models scale unexpectedly, cloud bills spike, or multicloud complexity threatens to derail everything.

My focus: AI Strategy & Transformation — engineering multicloud architectures where FinOps, governance, and agentic workflows are built in from day one. Every pipeline, cost model, risk framework, and decision must serve real people — their trust, their outcomes, their day-to-day reality. Getting it right for them isn't optional; it's the north star that guides every choice.

I design systems that quietly pay for themselves — predictable costs, automated governance, intelligent optimization, and decisions that align technical excellence with financial accountability and long-term customer trust.

Because in the end, the most powerful AI system isn’t the one with the highest benchmark.
It’s the one that quietly pays for itself — while making customers feel truly seen, supported, and valued every step of the way.

Early-year contributions (Jan–Jul) were lighter while I provided full-time home hospice care for my mom during her final months with brain cancer. It was the hardest and most meaningful work I've ever done. I'm back to full capacity now, as recent activity shows.

FinOps-Driven Multicloud AI Architecture

• End-to-end multicloud platforms (AWS, GCP, Azure, Databricks) designed for AI workloads with native cost governance, tagging strategies, and automated commitments/reservations
• AI-powered FinOps systems: predictive cost modeling, anomaly detection, real-time optimization, and unit economics that reveal true spend per model, pipeline, or team
• Cloud-agnostic architectures that eliminate vendor lock-in while maximizing discounts, spot/preemptible usage, and cross-cloud rightsizing for GPU-heavy AI inference/training

Production MLOps & Agentic AI Delivery

• Reliable agentic AI and RAG pipelines deployed at scale: secure, observable, auto-remediating, with built-in cost controls
• Model serving, inference optimization, and hybrid batch/streaming workflows tuned for throughput, latency, and dollar-per-query efficiency
• Linux-native, Kubernetes-first MLOps platforms using Terraform IaC — production runs on hardened kernels and observability, not fragile notebooks

Data Engineering & Pipelines at Scale

• Petabyte-scale, cost-optimized data pipelines (Bronze → Silver → Gold medallion) with heavy SQL craftsmanship for transformation, analytics, and feature stores
• Streaming + batch systems engineered for predictable spend: intelligent partitioning, auto-scaling, and FinOps-aware design that reduces bills by architecture, not after-the-fact firefighting
• Multicloud data architectures with unified governance, lineage, and cost attribution across providers

Pushing the edges to stay ahead of the curve — interconnected bets on where multicloud FinOps, agentic MLOps, and cost engineering head next: from Earth-bound optimization to quantum-accelerated edges to physics-enforced orbital dominance.

• QuantConnect-Powered Quant Pipelines — Building production-ready algorithmic trading workflows on QuantConnect: backtesting multicloud cost-optimized strategies, integrating real-time data pipelines, SQL-driven feature engineering, and MLOps for live deployment. Focus: FinOps-aware quant systems that scale profitably without exploding compute spend. (6-project series in progress.)
• Quantum-Enhanced Optimization Experiments — Prototyping quantum-inspired and hybrid quantum-classical algorithms for portfolio optimization, anomaly detection in FinOps, multicloud resource allocation, and classification tasks using classical simulators and Qiskit/PyTorch. Exploring how quantum advantages could supercharge cost governance and agentic decision-making in AI infra by 2030+.
  • Foundation: Compact 30-line Qiskit quantum teleportation demo → https://github.com/TAM-DS/Quant11
  • Hybrid Quantum-Classical Classifier: Minimal HQNN (Qiskit EstimatorQNN + PyTorch TorchConnector) achieving 100% test accuracy on make_moons → https://github.com/TAM-DS/Quantum-Hybrid-Moons-Classifier
  • VQE Ground-State Energy Demo: Computing H₂ ground state in STO-3G basis using UCCSD ansatz + Hartree-Fock initial state. Reaches chemical accuracy (~ -1.852 Hartree, error <1 mHa vs exact classical) → https://github.com/TAM-DS/Quantum-Chemistry-VQE-H2
  • Additional quantum prototypes (cleanups in progress): [Quantum Project 3 Name/Link]
• Orbital AI Security & Infrastructure Analysis — Comprehensive framework modeling the shift to space-based AI: physics/economics of orbital compute (free radiative cooling, solar efficiency, tipping point at <$50/kg launch), threat propagation (RAG vuln 0.79 in retrieval layer), control strain under latency (human-in-loop fails at 0.91 via 100k Monte Carlo sims), and value inversion (autonomous control captures 92%). Predicting 25-40% exascale AI training in orbit by 2034-2037. Core insight: Continuity guaranteed by autonomy owns the orbital economy. Full series → https://github.com/TAM-DS/Orbital-AI-Security-Analysis-Series

• FinOps maturity from day zero — visibility, allocation, optimization, and continuous forecasting embedded in design
• Production mindset — systems are built to survive chaos, with SLOs, alerting, chaos engineering, and rollback as table stakes
• Intelligent cost engineering — treat dollars as a first-class metric alongside accuracy and speed
• Multicloud fluency — seamless portability, best-of-breed selection, and unified observability across clouds

• AWS Solutions Architect – Professional (SAP)
• Google Cloud Professional Machine Learning Engineer
• Databricks Machine Learning Professional
• FinOps Certified Practitioner

Let's talk production AI that pays for itself—and gets quantum-ready. 🚀

• Builder of systems that must work in production — iterate relentlessly, measure everything, eliminate toil, and optimize ruthlessly for scale and cost. Because great just isn’t good enough; build for the long game.

Production-grade system designs that survive real constraints — not just whiteboard sketches.

8 deep dives covering: • End-to-end MLOps pipelines
• Secure LLM systems (from first principles)
• Multi-cloud agentic AI deployment
• Kubernetes ML workloads
• Orbital autonomous control
• Medallion data lakehouses
• RAG at scale
• Data engineering foundations

Built to teach how systems behave under pressure — latency, drift, security, and 2am failures.

20-dashboard series exploring Texas as the emerging capital of AI infrastructure: grid power → megawatts → teraflops → orbital compute.

Tableau dashboards that push the boundaries of data storytelling.

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)

🌐 Portfolio • 💼 LinkedIn • 🐦 X • 📲 Join my WhatsApp Channel for exclusive PDFs, checklists, and weekly orbital AI insights:
https://whatsapp.com/channel/0029Vb6rVBD29757lPbMat3P

Shipping production systems that don’t wake you at 2am. Austin, Texas.