Description The ideal candidate brings solid Machine Learning and AI expertise, with hands-on experience building and operating data-driven and intelligent systems in production environments. Candidates must be comfortable defending architectural decisions and writing production-grade code in a high-performance environment.
Key Qualifications
Strong experience with Apache Spark, including optimization techniques for large-scale workloads Strong experience with Spark Structured Streaming Strong experience with Spark SQL and query performance optimization Hands-on experience with Python for data processing, ML, and AI use cases Practical experience in Machine Learning (ML), including model development and integration Solid understanding of AI / GenAI concepts, with ability to apply them in real-world scenarios Experience with Terraform and Databricks Asset Bundles AI / ML Focus (Added Emphasis)
Proven practical ML experience, not only theoretical knowledge Experience working with AI solutions before and/or during the GenAI era (classical ML is valuable) Ability to integrate ML/AI components into scalable data platforms Understanding of data pipelines supporting AI/ML workflows Nice to Have
Familiarity with Databricks (good working knowledge; deep expertise is a plus but not required) Experience with application development and Databricks Apps Comfort working in Linux environments Background in financial markets, such as market making, trading, or related domains Knowledge of German or French is a plus Working Style
Able to work independently, prioritize effectively, and proactively identify opportunities for improvement Comfortable operating in a loosely defined scope, contributing not only to execution but also to defining priorities and driving work forward
