Vetted Education Professionals

“Robotics/control-focused candidate with hands-on ROS2 + Gazebo experience implementing MPC with online state identification on a Crazyflie drone, including camera-based position determination fixes. Also worked on multi-agent spacecraft formation control and constellation optimization, debugging numerical drift and redesigning leader-follower control laws to handle delayed/outdated updates; uses Docker to ensure reproducible simulation results across machines.”

“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”

“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”

“Built and productionized a dual-agent LLM invoice-processing system for GFI Partners, adding guardrails and audit trails to earn stakeholder trust and drive adoption while cutting operational burden by 75%. Uses LangSmith observability to diagnose real-time workflow regressions and has experience teaching agentic AI concepts (e.g., at Carnegie Mellon) through hands-on, scaffolded demos.”