Selected engagements with measurable outcomes — and the lessons that only show up when theory meets production at 3 a.m.
Case studies are where abstractions earn their keep. Not the polished version from a quarterly review — the version where latency budgets, compliance reviewers, and tired engineers all have a vote.
Each story below is a snapshot of that reality: the challenge as it actually arrived, the approach we took, and what changed on the other side.
Accelerate responsible Generative AI adoption across engineering teams in a highly regulated healthcare environment.
Led platform strategy (Databricks + Snowflake), built AI governance frameworks, scaled prompt engineering and agentic development training, and established observability standards.
Measurable improvements in data accuracy, team productivity, and responsible AI practices across multiple engineering groups.
Replace legacy batch claims processing with a real-time event-driven architecture while maintaining zero downtime for mission-critical operations.
Designed Kafka-based event pipelines, incremental migration strategy, and comprehensive observability with automated rollback capabilities.
Reduced claims processing latency from hours to seconds and established the infrastructure layer that later enabled AI-powered claims assistance.