About
Background & approach.
Background
Who I am.
I'm an AI-Native Engineer with a Senior SDET background, building agentic AI systems and the evals, guardrails, and quality practices that make LLM products reliable in production. Nine years across healthcare, enterprise SaaS, and developer tooling means I bring deep quality engineering instincts to AI-native product work, things like LangGraph agents, MCP integrations, RAG with pgvector, and human-in-the-loop workflows.
My approach treats AI as a collaborator, not a replacement for judgment. The most useful AI features are the ones that ship, stay observable, and earn user trust, which means owning the whole stack from prompt and context design through retrieval, orchestration, evals, and the product surface where users actually meet the model.
Principles
How I build reliable AI.
Reliable AI
Guardrails, evals, monitoring, and human-in-the-loop turn LLMs into production systems instead of demos. Quality is the difference between an AI feature that ships and one that gets rolled back.
AI as a collaborator
Agents accelerate humans, they don't replace judgment. The best AI features amplify expert decisions, leave audit trails, and stay legible to the people who own the outcome.
End-to-end ownership
Shipping AI-native features means owning the whole stack, prompt and context design, retrieval and embeddings, orchestration, evals, and the product surface where users actually meet the model.
Cross-functional collaboration
The best systems are built when engineers, product, design, and domain experts are aligned early. Quality comes from shared understanding, not from a final QA gate.
Smart automation
Automation should free teams for the work humans do best, exploratory testing, ambiguous edge cases, and product judgment, while critical paths stay covered without manual effort.
Continuous improvement
AI systems drift, models change, and prompts decay. Tight feedback loops, regression evals, and observability keep features honest as the underlying technology moves underneath them.
Stack
Tools I reach for.
Testing
Languages
Frontend
Backend
Cloud
Tools
Let's connect.
Always interested in discussing agentic AI systems, LLM evals and guardrails, AI-native product engineering, or potential opportunities.