Principal Machine Learning Engineer / Data Engineer

I build production ML and data systems in Python and AWS, with a focus on reliable deployment, data quality, and turning ambiguous business problems into useful products.

• Principal-level ML and data engineering: experimentation, deployment, monitoring, governance, and iteration
• Strong in AWS, Python, SQL, PySpark, CI/CD, and production data pipelines
• Comfortable owning systems end-to-end, from data integration to model serving and operational support
• Experienced partnering with product, engineering, analytics, and leadership to ship practical ML features
• Interested in remote roles and Europe-based opportunities, especially NL, Spain, France, and Switzerland

Around 2014, grinding a maze dungeon in an addictive mobile RPG got old fast. Every two weeks: new layout, manual farming for max rewards. So I built an AI bot on my home PC emulator to auto-solve mazes and handle combat; set it and forget it while I was away at work.

Turned out, coding the bot hooked me more than the game. Designing logic, iterating on pathfinding, testing edge cases... I realized people pay well for this. That sparked my pivot from running a tutoring business into Python, AI/ML, and production systems.

• ML systems that need to run in production, not just notebooks
• Data integration and analytics layers that support BI, ML, and operations
• GenAI / NLP applications with evaluation, monitoring, and business constraints
• Internal tooling, shared libraries, and engineering practices that improve team velocity

• Led ML workstreams at Nike on retail and supply-chain problems with measurable business impact ($10M+ annually), owning roadmap, stakeholder alignment, and production services
• Engineered shared Python libraries and tooling across Nike's Product Creation & Merchandising ML teams, enforcing standards for CI/CD, logging, monitoring, and code quality
• Acted as liaison between Nike's Global Tech MLOps/Engineering and Consumer Data Science orgs, aligning platforms and processes to accelerate ML delivery
• Built and owned AI/ML and MLOps strategy at Moleaer, including data pipelines (HubSpot/SharePoint/NetSuite), AWS governance, and production model deployment
• Mentored engineers and data scientists across teams, reviewing designs/code and contributing to internal ML knowledge sharing

Python · SQL · AWS · PySpark · MLflow · GitHub Actions · CI/CD · Docker · PostgreSQL · Pandas · NumPy · scikit-learn · Hugging Face · RAG · LangChain · TensorFlow · PyTorch · Databricks

• Principal Machine Learning Engineer, Moleaer
• Senior / Lead Data Scientist, Nike (strongest team culture and WLB)
• Machine Learning Engineer, DHL Supply Chain
• Data Scientist, Pandata

LinkedIn: https://www.linkedin.com/in/davidkasparworks/ Email: datakaspar@gmail.com