Alisa Dammer is a versatile machine learning and software engineer with 12 years of experience building reliable, production-grade systems across backend, DevOps, analytics and ML. Based in Hamburg, she moves fluidly between Python and Rust, infrastructure-as-code (Terraform, Ansible) and ML engineering (MLflow, feature engineering, model monitoring), with hands-on experience deploying both classical and LLM-based solutions. Her background includes high-performance simulation and ETL work at mobility platforms and production-focused ML roles where she implemented automated data-quality pipelines, CI/CD, testing and retraining loops. She gravitates to solving core problems and chooses new tools pragmatically—adopting “the shiny” only when it yields better outcomes. As a longtime generalist who has taken multiple hats, she blends deep technical rigor with a pragmatic, metrics-driven approach to model and system performance. Outside standard roles she’s known for pairing simulation-style thinking with ML performance assessment to create transparent, measurable feedback loops.
12 years of coding experience
6 years of employment as a software developer
Bachelor's Degree, Information Systems, Bachelor's Degree, Information Systems at University of Hamburg
Bachelor's Degree, Economics, Finance, Bachelor's Degree, Economics, Finance at Novosibirsk State University (NSU)
core functionality to collect personal data and run initial analysis
Contributions:1 review, 151 commits, 40 PRs in 10 months
collectinitialpersonal-data
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