Build and maintain production ML systems: Contribute to existing ML-based services, from training pipelines to production performance optimizations.
Own services end-to-end: Design, build and maintain production services
from a PoC all the way to a functional cloud deployment.
Partner with product and data science: Work with PMs, data scientists, and other engineers to translate research and business questions into shippable ML.
Understand the platform: To facilitate the above, you’ll work in Databricks, AWS and other tooling to be able to analyze, understand and present the data, metrics and insights needed to help make the best decisions for the platform.
Contribute to the team's standards: Review not just code but the ML side of the work as well
evaluation standards, model documentation etc.
Leverage AI tooling: Use Claude Code and agentic programming tools to accelerate the boilerplate so you can focus on the modelling decisions that actually matter.

Excellent programming skills and proficiency in Python.
Knowledge of Java is a plus.
Knowledge of traditional machine learning tools and techniques (e. g. support vector machines, gradient boosting)
An understanding of LLMs and their architecture, ideally with experience in fine-tuning, e.g. LoRA, distillation is a plus
Proficient in SQL, knowledge of Databricks is a plus
Familiarity with cloud technologies, e.g. AWS
Proven experience as an AI/ML Engineer
Outstanding problem-solving and analytical skills
Knowledge of maths, probability, statistics and algorithms

AI/ML Engineer

Key skills