Summary
Gabriel Homsi is a Senior Machine Learning Engineer based in Montreal with 11 years of experience building production-ready data and optimization systems that span ML, operations research and geospatial analytics. He combines a PhD-level research background with hands-on engineering across Spark/Databricks, Snowflake/BigQuery, Airflow/dbt and cloud-native deployments (AKS, Docker, CI/CD), enabling end-to-end pipelines from feature engineering to model monitoring. His work blends decision-support optimization (vehicle routing, facility location, stochastic programming) with modern ML—LLMs, explainable AI and time-series forecasting—applied to energy and transportation problems at Natural Resources Canada and in industry. Gabriel has repeatedly translated research into impact: shipping geoprocessing tools for network optimization, authoring peer-reviewed work, and securing grant funding while collaborating with partners like IBM and academic labs. Comfortable in many languages and stacks (Python, C/C++, Java, R, SQL, GIS tooling), he’s as effective prototyping novel algorithms as he is productionizing robust, scalable systems. An intriguing throughline in his career is pairing classical OR techniques with generative and explainable ML to solve real-world logistics and net-zero infrastructure challenges.
11 years of coding experience
13 years of employment as a software developer
High School Diploma, Computer Science Specialization, High School Diploma, Computer Science Specialization at Escola ORT
PhD, Computer Science, PhD, Computer Science at Université de Montréal
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at UERJ
Exchange Student, Computer Science, Exchange Student, Computer Science at Eindhoven University of Technology
Pontifical Catholic University of Rio de Janeiro
Portuguese, English, French, Spanish