Path to Impact

Architected enterprise GenAI platform serving 15K+ internal users, reducing policy-query resolution time from 4 hours to under 90 seconds. Built Python-based document parsers processing 50K+ pages/month with 97.2% extraction accuracy. Deployed RAG pipeline via FastAPI + Azure AI Search achieving p95 latency of 2.4s at $0.42/1K requests.

Led ML engineering team of 8 engineers to deliver 12+ production data pipelines, reducing model training time by 65% through PySpark optimization. Implemented MLOps framework that increased model deployment frequency from quarterly to weekly. Achieved 94% model accuracy in customer churn prediction, saving $2.3M in retention costs.

Engineered scalable PySpark pipelines processing 10M+ records/day, improving ETL performance by 78% and reducing job failures by 92%. Developed real-time dashboard serving 500+ concurrent users with sub-second query response. Optimized database queries achieving 3.2x faster report generation for 25+ enterprise clients.

Led cloud data migration for 6TB of enterprise data across 3 AWS regions, completing 3 months ahead of schedule with 99.8% data integrity. Implemented predictive analytics models that increased sales forecasting accuracy by 43% and reduced inventory costs by $1.1M annually. Automated 85% of manual data migration tasks, saving 40+ hours/week of engineering time.