Engineer, ML Platform

• Design and build scalable backend services and APIs to support ML experimentation, deployment, and monitoring.
• Re-architect and rebuild existing data pipelines to handle real-time data processing and support production ML models.
• Develop tools that make it easier for ML engineers to design, test, and deploy ML pipelines.
• Ensure platform services are robust, secure, and meet high standards for performance and reliability.
• Collaborate with ML engineers, data scientists, and product teams to understand their needs and translate them into platform capabilities.
• Drive adoption of ML best practices and promote reusability of tools across teams.
• Contribute to the overall architecture and vision of the ML platform, ensuring it can scale with the company's growth.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

A leading entertainment technology company in Madrid is seeking a Senior Engineer for their ML Platform. The successful candidate will design scalable backend services to support machine learning initiatives, contributing to the development of an innovative platform. Ideal applicants have over 7 years of experience in backend engineering, with strong Python skills, and experience with real-time data processing frameworks. This is an exciting opportunity to join a growing team in a dynamic organization., * 7+ years of professional experience as a Backend Engineer, Software Engineer, or similar role.

• Strong programming skills in Python.
• Proven experience designing and building distributed systems and APIs at scale.
• Hands-on experience with real-time data processing frameworks (e.g., Kafka, Flink, Spark Streaming, Pulsar).
• Experience working with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes).
• Strong understanding of data modeling, storage systems, and streaming/processing architectures.
• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Familiarity with ML model lifecycle management (training, deployment, monitoring).
• Excellent collaboration and communication skills, with the ability to work cross-functionally.