Our ML Platform Infrastructure team provides high speed intelligence for AI Systems across search, recommendations, personalization, forecasting, and emerging GenAI experiences. We focus on low-latency features, embeddings and real-time updates. We operate a feature store platform that we are scaling beyond just a feature store to become Zalando’s central discovery hub to serve customer, product and content understanding in real time. As Senior MLOps Engineer, you will be the most experienced engineer in this area, shaping the technical strategy, standards, and architecture for the future ML platform capabilities, and unlocking customer insights and predictive trending
INCLUSIVE BY DESIGN
At Zalando, our vision is to be inclusive by design. And this vision starts with our hiring - we do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status. You are welcome to leave out your picture, age, or marital status from your application. We only assess candidates on their qualifications and merit.
We want to provide you with a great candidate experience. Feel free to inform us of any accommodations you may need, so we can best support you throughout the hiring process.
- do.BETTER - our diversity & inclusion strategy: https://corporate.zalando.com/en/our-impact/dobetter-our-diversity-and-inclusion-strategy
- *Our employee resource groups: https://corporate.zalando.com/en/our-impact/our-employee-resource-groups
WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)
ML Subject Matter Expertise: Contribute to defining golden paths and best practices for building scalable, real-time systems.
Drive platform reliability: Define and implement SLOs for feature freshness, backfill correctness, and online/offline consistency; establish monitoring and alerting strategies; and help enable safe deployments and fast rollbacks.
Embed security by design: Implement identity and access management, secrets handling, network isolation, and data governance within platform workflows and self-service tooling.
Scale developer productivity: Champion automation and self-service (IaC, GitOps, CI/CD), create comprehensive documentation, and assist in designing onboarding programs that reduce time-to-first-success for scientists and engineers.
Provide technical guidance: Serve as a technical resource for complex ML infrastructure challenges, mentor junior engineers, and contribute to team-wide engineering standards.
Contribute to strategic decisions: Partner with product and engineering management on infrastructure decisions and contribute to the long-term infrastructure strategy.
Champion hiring excellence: Participate in hiring and developing engineers.
WE’D LOVE TO MEET YOU IF YOU HAVE
Proven ML platform experience: 3+ years of experience building and operating ML/MLOps or large-scale platform infrastructure with a strong track record on AWS (EKS) (or equivalent cloud). Experience with mission-critical systems is a plus.
Feature platform or data transformation expertise: Strong experience with feature engineering pipelines or the willingness to build that experience, including data contracts, schema evolution, and managing online/offline consistency. Prior experience with feature stores (e.g., Feast, Hopsworks, SageMaker Feature Store) or equivalent data transformation platforms for ML can make you an ideal candidate.
Containers, orchestration, and data systems: Hands-on with Docker, Kubernetes, and data/streaming technologies (e.g., Kafka/Kinesis, Spark/Flink) supporting both batch and streaming ML.
Model serving and low-latency systems: Practical experience with production serving stacks (e.g., KServe/Triton/TorchServe/custom), request shaping, caching, and traffic management.
Observability and reliability: Experience implementing metrics/tracing/logging; and participating in incident response, capacity planning, and post-incident reviews for ML systems.
Security and governance: Proficiency with IAM, secrets, network boundaries, data protection, and compliance within platform workflows.
Excellent collaborator and communicator: Comfortable engaging with engineers, scientists, and product teams; able to translate research and product goals into easy-to-use, reliable platform capabilities.
BONUS / NICE TO HAVE
Streaming and batch data for ML: Partner with customer teams to enable consistent online/offline features, robust data contracts, and schema/versioning practices that reduce drift and breakage.
Vector search and retrieval: Experience with similarity search and embeddings in production (e.g., FAISS, Milvus, OpenSearch KNN, pgvector) and their integration into ranking/retrieval workflows.
GenAI infrastructure: Experience with LLM serving, prompt management, or RAG architectures at scale.
If you think you have what it takes, we encourage you to apply even if you don't meet every single requirement. You may just be the right candidate for this or other roles.
OUR OFFER
Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer. Learn all about Zalando and our values here: https://jobs.zalando.com/en/?gh_src=22377bdd1us
Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Zalando Lounge, discounts from external partners
2 paid volunteering days a year
Hybrid working model with 60% remote per week, actual practice is up to each team to best support their collaboration
Work from abroad for up to 30 working days a year
27 days of vacation a year to start
Relocation assistance available (subject to prior agreement)
Family services, including counselling and support
Health and wellbeing options (including Gympass)
Mental health support and coaching available
