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.

Quick 2025 context
Early-year contributions were lighter (Jan–Jul) while I provided full-time home hospice care for my mom during her battle with brain cancer. During that time, I remained actively engaged—closing three clients and delivering five projects—while narrowing scope to protect quality and outcomes. I returned to full operating cadence mid-year and have since been shipping at high velocity across FinOps-driven multicloud AI systems, cost-aware quant pipelines, quantum optimization prototypes, and agentic infrastructure.

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.