Andreas Divaris is a seasoned software engineer and quant trader with 15 years building backend systems, ETL pipelines, and ML-enabled services across startups and healthcare genomics. He blends a systems-thinking approach—focused on the interconnections between teams, tooling, and delivery—with hands-on expertise in Python, FastAPI, Airflow, Kubernetes and emerging TypeScript work. Andreas has driven measurable impact from productionizing ML for genetic variant interpretation to automating labeling workflows with GPT-4, and now applies those skills to algorithmic trading and real-time sentiment services. Equally comfortable architecting observability and CI/CD practices as he is designing resilient data pipelines, he favors self-managed team structures and inventive org design to scale engineering culture. Based in the San Francisco Bay Area, he pairs curiosity about complexity theory with a pragmatic habit of experimenting with new tools to find the most effective engineering approaches.
10 years of coding experience
17 years of employment as a software developer
California Polytechnic State University, San Luis Obispo
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