Python/ Machine Learning Engineer (Regular/Senior)

VirtusLab is part of the expanding VirtusLab Group, offering a dynamic culture rooted in strong engineering, ownership, and transparency. The role involves working on projects in Forecasting & Commodities, supporting critical decision-making processes with a technically mature ecosystem.

• Strong experience writing high-quality Python code and deploying production-level projects.
• Significant experience in Data Engineering, specifically with PySpark, data quality monitoring, and workflow orchestration.
• Proficiency in Azure or equivalent cloud providers.
• Hands-on experience with Infrastructure as Code principles.
• Practical experience automating CI/CD pipelines using GitHub Actions.
• Proactive with a strong sense of ownership.
• Excellent communication skills and ability to work in a cross-functional, collaborative environment.
• Good command of English (B2/C1 level).
• Availability for a hybrid work model (2-3 days per week in Kraków office) or on-site collaboration approximately once a month.

Responsibilities:

• Develop libraries, tools, and frameworks to standardize and accelerate machine learning model development and deployment.
• Work in an Azure cloud environment, developing model training code in AzureML.
• Build and maintain cloud infrastructure using Infrastructure as Code.
• Use distributed data processing tools such as Spark for parallel computation in Machine Learning.
• Diagnose and resolve technical issues to ensure availability of high-quality, adaptable solutions.
• Collaborate with engineering and data science teams, providing advice and technical guidance.
• Champion best practices in code quality, security, and scalability.
• Make informed decisions to advance business objectives.

Project Challenges:

• Build systems providing accurate and up-to-date business forecasts.
• Streamline onboarding, deployment, and patching of new ML pipelines.
• Collaborate with cross-functional teams to enhance customer experiences.
• Employ DevOps practices for reproducible patterns across business domains.