GenAI/ML Engineer

About the role

We are seeking a senior software engineer with strong machine learning and Generative AI expertise to deliver end‑to‑end solutions for financial services clients. You will own the full lifecycle, from data pipelines and model development to production deployment and monitoring, working autonomously in regulated environments.

Key responsibilities

• Design and build end‑to‑end data pipelines and feature‑engineering workflows on large‑scale transactional data.
• Train, calibrate, and validate supervised, unsupervised and ensemble ML models, ensuring production‑grade explainability.
• Develop GenAI‑powered solutions such as agentic workflows, Retrieval‑Augmented Generation (RAG) systems, and LLM‑based automation.
• Deploy and monitor models in production with drift detection, performance tracking, and multi‑environment release management.
• Collaborate directly with client stakeholders, including compliance SMEs and engineering teams, delivering documentation and knowledge transfer.

Required profile

• Strong software engineering foundation: Python, Spark, CI/CD, containerised deployments (Kubernetes), and pipeline orchestration (Airflow or equivalent).
• Deep data‑science expertise: feature engineering at scale, statistical modelling, experiment design, and model explainability (e.g., Shapley values).
• Hands‑on ML experience with tree‑based methods, neural networks, ensemble design, hyper‑parameter optimisation, cross‑validation and out‑of‑time testing.
• Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflow development.
• Fluent German and strong client‑facing communication skills, capable of producing audit‑ready documentation for regulated environments.

Required skills

• Python
• Spark
• CI/CD
• Kubernetes
• Airflow (or equivalent)
• Feature engineering
• Statistical modelling
• Experiment design
• Model explainability (Shapley values)
• Tree‑based methods
• Neural networks
• Ensemble design
• Hyper‑parameter optimisation
• Cross‑validation
• Out‑of‑time testing
• LLM integration
• Prompt engineering
• RAG architectures
• Agentic workflows