Zoolatech
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 working on the Home page. 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. Responsibilities:
Design and build ML components, including systems for data access, feature management, model training, deployment, and inference at scale. Develop infrastructure and tooling that enable ML practitioners to experiment, version, deploy, and monitor models in a reliable and automated way. Architect scalable, modular, and reusable systems that serve as the backbone of ML development across multiple teams. Implement core abstractions and APIs — from feature creation to model rollout. Build and maintain observability and reliability tooling for ML systems — including telemetry pipelines, model health checks, and automated retraining triggers. 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. Qualifications:
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 Experience with Infrastructure-as-Code tools (CloudFormation, YAML) 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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Machine Learning Engineer (MLE/MLOps Focus)
to join a fast-moving and impactful team working on the Home page. 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. Responsibilities:
Design and build ML components, including systems for data access, feature management, model training, deployment, and inference at scale. Develop infrastructure and tooling that enable ML practitioners to experiment, version, deploy, and monitor models in a reliable and automated way. Architect scalable, modular, and reusable systems that serve as the backbone of ML development across multiple teams. Implement core abstractions and APIs — from feature creation to model rollout. Build and maintain observability and reliability tooling for ML systems — including telemetry pipelines, model health checks, and automated retraining triggers. 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. Qualifications:
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 Experience with Infrastructure-as-Code tools (CloudFormation, YAML) 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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