Summary
Ibrahim Ahmed is a software engineer and scientific ML practitioner with 11 years of experience building ML-driven control systems, optimization pipelines, and infrastructure for energy and AI applications. Currently at Google, he focuses on reliability and performance optimization for AI infrastructure, following recent roles that productionized MPC, RL, and LLM-based agents for HVAC, datacenter digital twins, and predictive maintenance. His PhD work developed reinforcement-learning controllers and embedded flight software for drones and smart buildings, pairing physics-based simulations with end-to-end ML pipelines. He has a strong track record of shipping research into production—reducing cooling energy, speeding up robot route planning, and deploying privacy-preserving, on-device ML prototypes. Fluent across Python, C++, Modelica/Simulink, and web/3D tooling, he blends systems engineering with scientific modeling to close the gap between simulation and real-world deployment. An uncommon strength is his habit of building lightweight tooling (ETL, logging, and simulation libraries) that materially improves trust and deployability of ML controllers.
11 years of coding experience
5 years of employment as a software developer
The University of Edinburgh
National University of Sciences and Technology (NUST)
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Vanderbilt University