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Machine Learning Engineer
You will be part of an internationally skilled team comprising data scientists, analysts, and machine learning/data engineers. Your work takes place in an agile environment, contributing to the global goals of the organization. The Orchestrator program is more than just a technological marvel – it manifests a vision for a customer-centric, intelligent, and globally aligned banking experience. Whether it’s the personalized customer experiences, AI-powered insights driving decisions, the program’s global reach, or the innovative use of recommender engines, the Orchestrator symbolizes the potential of modern banking. It blends technology with empathy, efficiency with engagement, and, above all, reflects a relentless pursuit of excellence reflecting core values and aspirations. Form of employment – Permanent Location – Warsaw, Poland (Hybrid)
We are looking for you if you have:
Proficient knowledge of Spark and Python.
Sound understanding of deployment and consumption patterns of ML models.
Capability to refactor, maintain, and debug existing machine learning solutions.
Proficiency in enhancing and maintaining templates for the productionization of ML solutions.
Experience in constructing containerized components.
Familiarity with deployment and provisioning automation tools such as Docker, Kubernetes, and CI/CD.
Strong problem-solving skills, with the ability to troubleshoot and optimize ML models.
Excellent communication skills and a collaborative team player.
You’ll get extra points for:
Knowledge of Airflow, GCP (Dataproc, BigQuery, GCS), and Azure pipelines.
Your responsibilities:
Developing and maintaining a code base that generates ML models for various business units.
Continuously refactoring and simplifying complexity, such as transitioning from batch to stream processing.
Enhancing data flow, introducing new data sources, and managing deployment processes.
Addressing data issues and providing robust solutions to ensure optimal performance and reliability.
Ongoing optimization of systems for performance and cost-effectiveness.
Documentation of technical specifications, procedures, and outcomes.
Utilizing Azure DevOps for CI/CD, task tracking, version control, and other DevOps practices.
