Candidate: Pranav Tanaji Ghadge

The most complex inference system I’ve managed was a real-time demand forecasting pipeline designed to serve enterprise-scale transactional data with a sub-200ms latency SLA. The core model was a gradient-boosted ensemble trained on roughly 1.5TB of data (60M+ records).

The Stack: I used AWS SageMaker for model hosting, S3 as the primary feature store, and Airflow to orchestrate micro-batch feature updates. This allowed us to maintain prediction freshness without the overhead of heavy real-time feature engineering.

Key Optimizations: