• Developed production machine learning models using Core ML, PyTorch, and XGBoost to power intelligent features across Apple Intelligence, supporting 50M+ daily inference requests with sub-100ms on-device latency.

• Built large-scale feature engineering pipelines using Apache Spark and AWS EMR, integrating behavioral signals, usage telemetry, and privacy-preserving federated data from 340M+ active devices.

• Designed dual-model prediction architecture using XGBoost (AUC 0.92) and LightGBM (AUC 0.85) for personalized recommendations and anomaly detection across Siri, App Store, and Apple Pay services.

• Implemented model explainability using SHAP aligned with privacy-first AI principles, generating transparent model insights for ML governance, risk analysis, and compliance auditing.

• Engineered fraud and anomaly detection system using PyTorch autoencoders and LightGBM, analyzing 85M+ Apple Pay and App Store transactions, reducing false positives by 40% while maintaining PCI-DSS regulatory compliance.

• Built scalable MLOps infrastructure using AWS SageMaker, Apache Airflow, MLflow, Docker, and Kubernetes, enabling automated model training, deployment, versioning, and data drift monitoring using PSI metrics.

• Built recommendation ranking models using LightGBM (LambdaMART) in a two-stage personalization system processing 200M+ user-item interactions, improving homepage CTR by 23% and add-to-cart conversions by 11%.

• Developed scalable feature engineering pipelines with PySpark, Hadoop, and Hive processing 15M+ daily clickstream events, generating 300+ behavioral features, user embeddings, and item affinity scores.

• Implemented real-time ML inference infrastructure using FAISS, Redis feature store, and Kubernetes microservices, supporting 1.2M+ recommendation API requests per minute with p99 latency under 85ms.

• Designed Customer Lifetime Value (CLV) prediction models using LightGBM and XGBoost, scoring 200M+ users weekly, improving targeted marketing campaign efficiency by 28%.

• Automated end-to-end MLOps pipelines using Apache Airflow, Docker, and Azure HDInsight, enabling scheduled model retraining, feature computation, and batch inference workflows.

• Developed RESTful APIs using Python, Django, and Django REST Framework with PostgreSQL for an inventory management platform managing 80,000+ SKUs across multiple warehouses.

• Built event-driven backend services using Celery and Redis for asynchronous task processing, eliminating 4-hour batch processing delays through real-time updates.

• Implemented concurrency-safe inventory reservation logic using PostgreSQL transactions and Django select_for_update(), preventing race conditions during payment and reservation workflows.

• Optimized PostgreSQL database performance through indexing, query tuning, and materialized views, improving API response times from 2+ seconds to under 100ms on datasets exceeding 5M records.

• Deployed containerized Python applications using Docker and implemented unit and integration testing with pytest, maintaining 80%+ test coverage and improving code quality and deployment reliability.