Vetted C++ Professionals

“Backend engineer on Amazon’s Geospatial Data team (Amazon Maps) who built a real-time road-layer service ingesting third-party and internal signals to deliver road closures/traffic overlays to delivery drivers on a ~3-minute cadence while minimizing mobile data egress. Demonstrates strong production reliability skills (rate limiting, idempotency, cache stampede prevention) and security depth (IAM, RBAC, tenant row-level security), plus careful handling of edge cases like manual override protection against automated feed overwrites.”

“Hands-on engineering leader with early-stage startup experience who balances coding with executive alignment and delegation. Has delivered offline-capable SaaS mobile demos under tight timelines, built monetization-ready mobile architectures (dynamic cloud assets + in-app purchases), and increased product velocity via component-based game architecture plus stakeholder self-serve tooling.”

“Robotics software engineer with hands-on ROS 2 experience who helped build an autonomous 5-DOF robotic arm that plays Backgammon, owning perception (OpenCV) and game-logic while adding robustness features like lighting tolerance and auto-calibration. Also worked on a Raspberry Pi/LiDAR car project, improving mapping accuracy through data-logged calibration and contributing to multi-robot collision-avoidance coordination via a server-based pub/sub system.”

“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”

“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”