I'm a full-stack software engineer with 5+ years of professional experience, specializing in AI integration and agentic systems. I build production AI solutions at companies like Verisk Analytics and ReliaQuest: systems that deliver measurable team impact, not just impressive demos. My approach is grounded in what actually works at enterprise scale.
My path to software began in biology labs, automating data analysis with Python. That foundation in complex systems thinking now shapes how I approach AI integration: understanding when emergent behavior adds value and when it creates unpredictable risk. Not every problem needs AI; recognizing the difference is half the battle.
At Verisk Analytics, I cut Playwright test code generation time by 40% using agentic AI tools and context engineering practices. At ReliaQuest, I work with enterprise agent orchestration systems for real-time security threat detection. These are production systems with real consequences, not experimental side projects.
I've also built my own agent systems from scratch: a Digital Twin chatbot with multi-agent orchestration, a Writers' Room with literary AI personas, and a Code Review Command Center with specialized analysis agents. Each project taught me something different about context engineering, prompt design, and the gap between demo-ready and production-ready. That gap is where most AI initiatives fail.
Current Focus
AI-Augmented Engineering
Integrating AI tools (Claude Code, Cursor, custom MCP servers) into development workflows with measurable productivity gains. Focus on sustainable adoption, not just initial velocity.
Agentic Architecture
Designing multi-agent systems with orchestration, handoffs, and guardrails. From simple tool-calling to complex workflows with specialized agents handling distinct responsibilities.
AI Implementation Strategy
Identifying high-value automation opportunities, scoping realistic timelines, and building phased rollout plans. Understanding where AI fails is as important as where it succeeds.
Context Engineering
Crafting effective prompts, system instructions, and retrieval strategies. The quality of AI output is directly proportional to the quality of context you provide.
How I Work
- Start with the problem, not the technology. AI is a tool, not a destination.
- Measure twice, deploy once. Validate AI solutions against business outcomes before scaling.
- Build guardrails first. Production AI needs error handling, rate limiting, and human oversight.
- Document the why, not just the what. Future maintainers need to understand your AI integration decisions.
- Stay close to the tools. The best AI strategies come from engineers who ship, not just advise.
Interested in exploring the future of AI-augmented development?