Vikram Singh is a seasoned software engineer and data-focused developer with 10 years of experience building scalable ETL/ELT systems and productionizing machine learning pipelines. With a PhD in Computational Fluid Dynamics from Technion and a dual degree from IIT Kanpur, he brings strong scientific rigor to engineering problems, having moved multi‑TB pipelines to Databricks and cut validation times from 40 hours to 1 with Kubernetes/Argo workflows. He is a pragmatic TDD advocate who optimizes for cost and performance—evidenced by a 20x cost reduction from NumPy-optimized PySpark UDFs and 10x faster hyperparameter workflows. Comfortable in Agile teams and cross-disciplinary research collaborations, he has shipped reproducible Python libraries and contributed to the AWS SDK for Pandas. Now working across AI and data roles in Hamburg, he blends deep-domain modeling experience (climate and aerodynamics) with hands-on big data engineering. Notably, his background in CFD and evolutionary design gives him an unusual ability to translate complex numerical models into production-ready data products.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Mechanical Engineering Computational Fluid Dynamics, Doctor of Philosophy - PhD Mechanical Engineering Computational Fluid Dynamics at Technion - Israel Institute of Technology
Contributions:22 commits, 18 pushes, 1 branch in 2 years 11 months
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