About

Practical AI Systems, Built for Real Organizations

I work with mid-market companies, enterprises, and nonprofits to design and deploy AI systems that are reliable, measurable, and aligned with real-world constraints.

My focus is not on demos or experimentation for its own sake. It's on AI systems that hold up in production—systems teams can trust, evaluate, and maintain.

Based in Austin, Texas. Working with organizations locally and across the U.S.

How I Approach AI Work

AI projects fail most often not because of the model, but because of unclear goals, weak evaluation, and unrealistic assumptions about how AI behaves in real workflows. My approach is shaped by that reality.

When I work with a team, I focus on:

  • Understanding the actual workflow, not the abstract use case
  • Designing systems with clear boundaries, failure modes, and escalation paths
  • Treating evaluation and safety as first-class concerns, not afterthoughts
  • Building solutions that balance technical capability, cost, and risk

This applies whether the work involves AI agents, RAG pipelines, or internal decision-support systems.

What I Bring to Engagements

Systems-Level Thinking

I design AI systems as part of a broader architecture—data flow, tooling, evaluation, and human oversight—not as isolated prompts or scripts.

Hands-On Implementation

I stay close to the technical work. That includes architecture design, implementation support, and iterative refinement based on real outputs and metrics.

Evaluation & Safety Mindset

A core part of my work involves defining how AI systems are tested, monitored, and governed. This includes structured evaluation approaches and practical guardrails.

Translation Between Stakeholders

I work across engineering teams, product/analytics, and leadership. The goal is alignment—shared expectations about what AI can and cannot do.

Professional Background

Senior AI Engineer (contract)

Building LLM safety guardrails, RAG ingestion pipelines, and evaluation frameworks for production systems.

Applied ML in Enterprise Environments

Including forecasting and decision-support systems where accuracy and reliability directly impact business outcomes.

AI Education and Leadership

Founder and leader of an Austin-based AI nonprofit focused on practical AI education, team training, and community programming.

Across roles, the common thread has been applied AI in real operational settings, not research prototypes or speculative concepts.

Who I Typically Work With

I'm a strong fit for organizations that:

Are serious about deploying AI responsibly
Need systems that work under real constraints
Value clarity, evaluation, and long-term maintainability

This includes:

  • Mid-market teams scaling AI beyond pilots
  • Enterprise groups deploying GenAI in sensitive workflows
  • Nonprofits seeking trustworthy AI systems with limited resources

What Working Together Looks Like

Engagements are collaborative and execution-focused. I aim to leave teams with:

A clear system design and rationale

Measurable ways to assess quality and risk

Internal understanding of how the system works

Some engagements are short and focused. Others evolve into deeper consulting or training relationships. The scope is driven by what actually creates value.

Availability

I'm available for:

AI consulting and implementation support
AI training for leadership and teams
Advisory or fractional AI leadership engagements

Most work begins with a short introductory conversation to determine fit and scope.

Book a 15-Minute Intro Call Contact