Senior MLOps Engineer Central Europe

Zoolatech; Central, Northern (LA, KY); Hybrid; USD 120,000 - 150,000; Full time

Job summary

Zoolatech seeks a Machine Learning Engineer (MLE/MLOps Focus) to join a fast-moving team that builds scalable ML workflows and production-grade systems.

You will design platform components for data access, feature management, model training, deployment, and monitoring, while collaborating with infra and security teams.

Qualifications

• 5+ years of experience in Machine Learning Engineering or MLOps.

Responsibilities

• Design and build ML platform components for data access, feature management, training, deployment, and inference.
• Develop infra and tooling to enable experiments, versioning, deployment, and automated monitoring.
• Create scalable, reusable ML components and workflows to speed model development and delivery.
• Standardize ML workflows from feature creation to rollout for reliability.
• Collaborate with infrastructure, data, and security teams to ensure production-grade ML systems.

Skills

CI/CD for ML

Model monitoring

Tools

Airflow

MLFlow

SageMaker

Grafana

Kubernetes

OpenAI API

Job description

Are you passionate about improving the way Machine Learning systems are developed, deployed, and scaled in real-world production environments? We are collaborating with a leading European Online Fashion & Beauty Retailer to find a highly capable and self-driven Machine Learning Engineer (MLE/MLOps Focus) to join a fast-moving and impactful team.

This role is centered around building robust ML workflows, streamlining feature creation, and standardizing ML components to ensure scalability, consistency, and speed across the organization. You’ll work at the intersection of engineering and data science, playing a key part in shaping how machine learning is delivered at scale.

Design and build ML platform components supporting data access, feature management, model training, deployment, and inference in production environments.

Develop infrastructure and tooling that enable ML practitioners to experiment, version, deploy, and monitor models in a reliable and automated way.

Build and improve scalable, reusable ML components and workflows that help teams efficiently develop and deploy models.

Contribute to standardizing ML workflows — from feature creation to model rollout to ensure consistency and reliability across teams.

Build and maintain observability and reliability tooling for ML systems, including model health checks and automated retraining processes.

Establish best practices, frameworks, and reference implementations that raise the bar for engineering rigor and speed in ML delivery.

Work closely with infrastructure, data, and security teams to ensure that ML systems are secure, compliant, and production-grade by default.

5+ years of experience in Machine Learning Engineering or MLOps roles

Strong hands-on experience with Airflow (MWAA), MLFlow, and/or SageMaker

Familiarity with ML observability tools such as Grafana, custom metric logging, model drift detection, and alerting mechanisms

Proficiency in building CI/CD pipelines for ML systems with automated testing and validation

Understanding of secure and compliant deployment of ML pipelines

Excellent debugging and problem-solving skills

Experience with OpenAI API usage in production, containerization, and Kubernetes orchestration is highly valued

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