Middle Data & MLOps Engineer

About the role

We are looking for a Mid‑level Data & MLOps Engineer to join our Data Science team. You will be responsible for building and maintaining the infrastructure that supports data processing, machine‑learning pipelines, and model deployment.

Key responsibilities

• Configure and support a JupyterHub environment, managing resources for the Data Science team.
• Design, implement, and monitor ETL/ELT and ML pipelines using Apache Airflow.
• Process large‑scale data sets with PySpark/Apache Spark and optimise performance.
• Containerise ML components with Docker and deploy them on Kubernetes clusters.
• Integrate models and data services via REST APIs, monitoring performance, drift and logs.
• Create and maintain CI/CD pipelines using GitLab CI or GitHub Actions.

Required profile

• Bachelor’s degree in Computer Science, Data Engineering or a related field.
• At least 2 years of hands‑on experience with Python and SQL.
• Proven experience building and managing workflows in Apache Airflow.
• Knowledge of big‑data processing and performance tuning with PySpark or Apache Spark.
• Experience deploying applications with Docker and Kubernetes.
• Familiarity with CI/CD tools and Linux environments.
• Ability to collaborate effectively with a Data Science team.
• Understanding of MLOps concepts such as model registry, deployment and monitoring.

Required skills

• Python
• SQL
• Apache Airflow
• PySpark
• Apache Spark
• Docker
• Kubernetes
• GitLab CI / GitHub Actions
• Linux operating system

What we offer

• Comfortable work environment with extensive professional development opportunities.
• Optional voluntary medical insurance program.
• A friendly, collaborative team culture focused on mutual support.