Senior MLOps\\ML Platform Engineer Central Europe

Zoolatech; Central (LA); On-site; USD 140,000 - 180,000; Full time

Job summary

Zoolatech is building a shared ML platform to enable product teams to develop, deploy, and scale ML solutions across the organization.

This platform-level role blends hands-on engineering with architectural input to define how ML infrastructure operates at scale, from proof of concept to early production adoption. You will collaborate with Applied Scientists, ML Engineers, and Platform teams to evaluate capabilities and design reusable components for rollout.

Qualifications

• 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 (Kafka, Spark, Flink).
• Experience with ML lifecycle orchestration and workflow systems (Metaflow, 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 (Databricks or similar).
• Experience onboarding teams onto shared platforms and optimizing infrastructure costs.

Responsibilities

• 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

Skills

Distributed systems

Kubernetes

ML lifecycle orchestration

Performance monitoring

Architectural design decisions

Cost optimization

Communication of trade-offs

Tools

Docker

Kafka

Spark

Flink

Airflow

Kubeflow Pipelines

Dagster

Flyte

Prefect

Databricks

Job description

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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