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I build and ship AI systems that work in production, not just in notebooks. My work sits at the intersection of technical architecture, product strategy, and client delivery. I've owned problems end-to-end: scoping ambiguous requirements, writing production code, aligning cross-functional teams, and presenting outcomes to executive stakeholders. I operate the same way regardless of whether the role says Product, Engineering, or Strategy. The work is the work.
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RAG architectures · Vector databases · API design · CI/CD · Distributed systems |
Roadmap ownership · PRDs and technical specs · Data-driven prioritization · Competitive positioning · Pricing and packaging · Go-to-market execution · Stakeholder alignment · Experimentation frameworks |
Enterprise sales engineering · Executive communication · Cross-functional leadership · Vendor evaluation · Technical due diligence · Workshop facilitation · Partnership development |
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Real-time observability pipeline for multi-agent LLM systems. Traces agent decision paths, tracks token-level costs, and surfaces hallucination rates. Built so engineering and finance teams can set SLAs and control inference spend at scale.
Impact: Unlocks enterprise adoption by making cost and reliability measurable from day one. |
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RAG-powered system that replaces keyword-based resume filtering with semantic matching against job descriptions. Cuts top-of-funnel noise and lets recruiting teams focus on high-signal candidates.
Impact: Directly compresses time-to-hire, one of the most expensive metrics in talent acquisition. |
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Conversational AI for symptom triage and appointment routing. Designed around HIPAA-compliant architecture: no PHI logging, local inference options, and explicit user consent flows baked into the core product.
Impact: Purpose-built for regulated environments where trust is the product, not a feature. |
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Bias toward action I don't wait for perfect information. I scope quickly, ship a working version, and iterate based on real signal: usage data, stakeholder input, system telemetry. |
Precision in communication I default to written proposals because they force rigor. When I present, I lead with the decision, not the background. |
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Technical credibility I read the papers, review the PRs, and prototype before I write specs. Understanding the system's constraints is how you avoid building the wrong thing. |
Full-stack ownership The highest-leverage task on any given day might be a SQL query, a deployment fix, or a GTM strategy doc. I don't distinguish between "product work" and "engineering work." |
Carnegie Mellon University · M.S. Information Systems Management
AI/ML Systems · Applied Machine Learning · Distributed Systems
I look for environments where technical depth, strategic thinking, and speed of execution are valued over job titles.