Senior MLOps\\ML Platform Engineer Europe

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

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