Data AI Engineer

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