This opportunity is part of a newly launched ML initiative within one of Europe’s leading fashion and retail companies, operating at large scale across multiple markets and serving millions of customers.

The team is building a shared Machine Learning platform that will enable multiple product teams to develop, deploy, and scale ML solutions reliably in production.

This is a platform-level role focused on evaluating, designing, and rolling out reusable ML capabilities across the organization - from proof of concept to early production adoption. The position combines hands-on engineering with architectural input, helping define how ML infrastructure will operate at scale across the company.

• Support and contribute hands-on to multiple ML platform POCs

• Work closely with Applied Scientists, ML Engineers, and internal Platform teams

• Evaluate platform capabilities (GPU training, real-time and batch inference, orchestration, scalability)

• Analyze performance, reliability, and integration with existing systems

• Contribute to key technical decisions (build vs buy, tooling, target stack)

• Support early production rollout and integration into the broader ecosystem

• Help define best practices, governance standards, and onboarding guidelines

• Enable adoption of the platform by initial and scaling teams

• 5+ years building and operating production-grade ML or large-scale data systems on cloud platforms

• Experience with distributed systems (Docker and Kubernetes)

• Experience with streaming or batch processing systems (e.g. Kafka, Spark, Flink)

• Experience with ML lifecycle orchestration and workflow systems (e.g. Metaflow / Outerbounds, Airflow, Kubeflow Pipelines, Dagster, Flyte, Prefect)

• Experience designing reproducible ML pipelines and supporting model training, evaluation, and deployment workflows across teams

• Strong understanding of reliability, monitoring, and safe deployment practices

• Experience integrating multiple tools into a coherent, production-ready ML platform

• Ability to contribute to architectural decisions and communicate technical trade-offs clearly

• Experience running ML workloads on Kubernetes (GPU or multi-tenant environments)

• Experience with enterprise ML platforms (e.g. Databricks or similar)

• Experience onboarding teams onto shared platforms and optimizing infrastructure costs