This is an exciting opportunity to join a newly launched ML initiative within a leading European fashion and retail company, where cutting-edge machine learning ideas are turned into real, production-ready solutions.

The role focuses on building and validating Machine Learning Proofs of Concept (POCs) that shape how ML is developed and scaled across the organization. It is a platform-level position spanning evaluation, implementation, and early production rollout. You will work in small, cross-functional project groups (together with applied scientists and software engineers) to design, build, and operationalize ML solutions that can later be reused and scaled by multiple teams.

• Design, build, and validate ML platform POCs across multiple use cases.

• Work closely with Applied Scientists, ML Engineers, and Platform teams to deliver end-to-end ML workflows.

• Implement and operate ML pipelines for training, inference, deployment, and monitoring.

• Run and optimize ML workloads on Kubernetes, including GPU-based and multi-tenant environments.

• Evaluate and integrate ML platform tools and infrastructure into a shared, scalable platform.

• Support the early production rollout and integration into the existing ecosystem.

• Define best practices, governance, and onboarding standards for teams adopting the platform.

• Ensure reliability, security, and performance of ML systems in production.

• Strong experience building and operating production-grade ML platforms or large-scale data/ML systems on cloud infrastructure.

• Solid background in distributed systems, including containers (Docker), orchestration (Kubernetes), and streaming / batch processing (Kafka, Spark, Flink, etc.).

• Experience designing and operating scalable, low-latency, or high-throughput systems.

• Strong understanding of reliability, monitoring, and safe deployment practices (SLOs, incident response, capacity planning).

• Experience embedding security, IAM, and governance into platform workflows.

• Ability to evaluate, integrate, and operate multiple platform components into a coherent ML platform.

• Strong communication skills, with the ability to produce architecture designs, POC findings, and technical recommendations.

• Experience with Kubernetes-first ML systems, including:

  • Running ML workloads on Kubernetes (EKS preferred)

  • Multi-tenant and GPU-based environments

• Experience with enterprise ML platforms (e.g. Databricks, Domino, ClearML).

• Experience with feature platforms / feature stores (Feast, Hopsworks, etc.).

• Familiarity with governance and compliance in regulated ML environments.

• Experience onboarding teams to shared platforms.

• FinOps awareness for ML infrastructure costs.

• Focus on developer experience and platform enablement (templates, golden paths, onboarding flows).