OVERVIEW
Responsibilities
Requirements 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:
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Running ML workloads on Kubernetes (EKS preferred)
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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).
Location:
Other, Central Europe
Seniority:
Senior
Technologies:
Python
Benefits:
Paid Vacation
Sick Days
Floating Holidays
Sport/Insurance Compensation
English Classes
Charity
Training Compensation