Sameer Chaturvedi

Prime Video -- Browse and Discovery

• Mixture-of-Retrieval ML models that proportionally promote content by genre and propensity.
• Custom logistic regression with stratified sampling + softmax tuning fixed flat predictions caused by 90%+ channel over-representation in training data; improved Share-of-Voice across genre carousels.
• Designed the end-to-end pipeline for FreshPicks, a carousel for new third-party content, driving +134K annualised subscriptions.
• Architected a cross-service content-filtering pipeline for subscription-bundle carousels with realtime entitlement parsing, token decoding, and multi-filter orchestration.

Plasma Science and Fusion Center

• Architecting DisruptionPy: a framework for analysing plasma-fusion data across five Tokamak reactors to predict and avoid disruptions using anomaly models.
• Revamping the backend to decouple data access from specific implementations; pluggable backends (MDSplus, Xarray/Zarr) through abstract interfaces for testability and multi-machine scalability.

Live Gameplay Team

• Full-stack build: frontend, API, LLM layer, and data pipeline all shipped by a small team.
• App lets coaches, scouts, and analysts interrogate live basketball-tournament stats via LLM chat, realtime graphs, and dashboards.
• Designed the pipeline that converts semantic text to SQL, queries the stats DB, processes data, and handles edge cases.

M365 Security Detections

• Petabyte-scale big-data platform using ML to detect cyber-attacks in near real time across all of M365.
• Lead engineer for Microsoft's first mail-anomaly detection (response to the nation-state incident where hackers accessed executive and federal emails).
• Lead engineer on a GPT-based security-detection assistant that analyses machine and identity activity for attack patterns.
• Designed the production workspace that enables data-science and LLM-backed detections in an agile PySpark environment with CI/CD and realtime job monitoring.
• Expedited detection programming by over 75% on distributed systems; auto-failover geo-replicated BCDR architecture.

Computer Vision Team

• Lead engineer on a deep-learning + iOS app for real-time object detection and instance segmentation.
• Improved accuracy with dropout, batch normalisation, and training on custom synthetic data.
• Converted models to run on-device via LibTorch; wrote the Objective-C++ bridge, hand-rolled NMS, and the full AVFoundation capture pipeline. CoreMotion integration plotted phone-path trajectory.
• Productionised the library and ETL pipeline with custom PyTorch DataLoaders and dataset mappers.

OpenShift -- Container Runtimes

• Upstream contributions to CRI-O (Kubernetes container runtime interface) and Libpod (container and pod manager).
• Unblocked engineers on image-volume write issues inside containers, fixed container-hook monitoring and execution, and added CLI env-var flag passthrough.