GenAI/ML Engineer

Role overview

Responsibilities

• check_circle Design and build end-to-end data pipelines and feature engineering workflows on large-scale transactional data in regulated client environments
• check_circle Train, calibrate, and validate ML models — supervised, unsupervised, and ensemble — including threshold analysis and production-grade explainability
• check_circle Develop GenAI-powered solutions such as agentic workflows, RAG systems, and LLM-based automation to accelerate knowledge-intensive client processes
• check_circle Deploy and monitor models in production with drift detection, performance tracking, and multi-environment release management (Dev Staging Prod)
• check_circle Work directly with client stakeholders — from compliance SMEs to engineering teams — delivering documentation, knowledge transfer, and long-term enablement

Basic qualifications

• Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)
• Data Science depth: feature engineering at scale, statistical modeling, experiment design, model explainability (e.g. Shapley Values), and threshold calibration under business constraints
• Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing
• Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes
• Fluent German and strong client-facing communication skills, with the ability to produce audit-ready documentation for regulated environments

About the company