Product Engineer and Precision Health Expert building full-stack AI systems that ship to production.
I am a Product Engineer and Global Senior Product Manager who thrives at the intersection of engineering, product thinking, and AI. I don't just write code; I own features end-to-end—from architectural design to production launch—focusing relentlessly on customer experience and business impact.
With over a decade of experience across the U.S. and Europe, I specialize in building AI-driven frontend experiences and foundational data platforms. My background spans scaling global health data systems serving over 2M patient records, leading LLM-based products that increased retention by 25%, and architecting API frameworks that improved reliability by 40%.
I excel in fast-paced environments where rapid iteration and outcome-driven development are paramount. Whether it's optimizing LLM latency or designing intuitive human-in-the-loop workflows, my goal is always to ship meaningful outcomes, not just features.
LLMs, Computer Vision, RAG pipelines, and human-in-the-loop systems.
Observability, monitoring, testing, and risk mitigation for critical systems.
Apogeee is a unified intelligence platform that consolidates AI models, data pipelines, and human-in-the-loop workflows into a single production-grade system. Designed for enterprise scale with real-time inference, multi-model orchestration, and full observability.
Real-time observability platform for distributed AI systems. Visualizes latency, throughput, and error rates with <50ms update frequency.
Enterprise AI platform delivering precision-tuned AI through intelligent model selection. Features sub-100ms response times and 99.9% uptime with intelligent failover.
SQL-native AI platform embedding AI capabilities directly into BigQuery. Unlocks insights from unstructured data in sub-500ms response times.
Production AI system for healthcare workflows with human-in-the-loop validation. Reduced manual effort by 25% while maintaining clinical accuracy.
Rapid prototyping and hackathon submissions showcasing ability to build and ship functional MVPs in limited timeframes.
Demonstrates how LLMs live inside product workflows, not just notebooks. Focuses on abstraction boundaries, human-in-the-loop logic, and provider-agnostic design.
def run_workflow(user_input: str) -> dict:
llm_result = call_llm(user_input)
if requires_human_review(llm_result["confidence"]):
return {"status": "needs_review", "result": llm_result}
return {"status": "approved", "result": llm_result}Demonstrates production thinking around AI risk with validation, fallbacks, monitoring, and guardrails.
def run_pipeline(input_data: dict):
if not validate_input(input_data):
return fallback_response("invalid_input")
if not enforce_guardrails(confidence, output):
return fallback_response("guardrail_violation")
return {"status": "success", "output": output}C++ implementation of a beat/pulsation algorithm, referencing medical definitions of stroke/seizure.
Forked contribution: Tensors and Dynamic neural networks in Python with strong GPU acceleration.
Forked contribution: Industrial-strength Natural Language Processing (NLP) with Python and Cython.
CONTACT
Interested in AI-powered products where engineering quality and customer experience both matter? Let's talk.
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