Python Engineer with MLOps Senior

VirtusLab Remote Kraków 2026-11-09
  • Solid Python skills with hands-on experience taking projects to production.
  • Practical experience with orchestration tools such as Airflow.
  • Background in distributed data processing, especially Spark.
  • Hands-on experience with Kubernetes.
  • Cloud experience (Azure preferred) and Docker containerization.
  • Smooth collaboration with cross-functional teams and hands-on involvement in technical design.
  • Clear and direct communication with both tech and business partner.
  • Readiness to share domain expertise and support other team members in technical growth.
  • High level of independence, pragmatic problem-solving, and a clear big-picture view.
  • English language proficiency at a minimum [B2/C1] level.
  • Primarily remote-based engagement with occasional on-site collaboration in Kraków (approx. once a month).

VirtusLab is a leading European software consulting and engineering company. Our mission is to craft clean code and practical solutions with precision and purpose. We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering professionals to make a substantial impact in the software industry.

An opportunity to drive business innovation through robust, production-ready machine learning pipelines. The main focus involves deploying and maintaining ML workflows across Azure cloud infrastructure and on-prem ETL clusters. The role centers on technical collaboration with Data Scientists to build AI-driven solutions and refine internal engineering standards.

Project Scope

The ML Engineer / Forecasting & Commodities engagement covers key initiatives supporting critical decision-making processes through the application of Python, PySpark, Kubernetes, and Azure Cloud technology stacks. The scope involves operating within a highly mature technical ecosystem, delivering new features, and scaling solutions to encompass advanced use cases. Key project focus areas include designing and implementing a data science innovation framework while contributing to the organization’s overarching engineering best practices.

Tech Stack

Python, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git @ GitHub

Project Challenges

– Building high-accuracy business forecasting systems by delivering reusable toolsets tailored for data scientists and analysts.
– Streamlining onboarding, deployment, and automated patching pipelines for new ML models.
– Partnering with cross-functional units to enhance end-customer experience through cutting-edge technology integration.
– Applying robust DevOps methodologies to establish reproducible patterns across multiple business domains.

Team

3 engineers

A few perks of being with us

Building tech community
Flexible hybrid work model
Home office reimbursement
Language lessons
MyBenefit points
Private healthcare
Training Package
Virtusity / in-house training
Access to the above perks is optional and completely voluntary for B2B contractors

,[Developing libraries, tools, and frameworks to standardise and accelerate the end-to-end development and deployment of machine learning models., Operating within an Azure cloud environment, including developing model training code in AzureML and building/maintaining cloud infrastructure via Infrastructure as Code (IaC)., Utilizing distributed data processing tools such as Spark to parallelise machine learning computations across large-scale datasets., Diagnosing and resolving technical issues, ensuring the delivery of high-availability, reusable, and adaptable engineering solutions., Collaborating with cross-functional engineering and data science teams to deliver technical guidance and optimize core workflows., Fostering industry best practices in code quality, security, and scalability., Autonomous, data-driven technical decision-making aimed at driving business growth.] Requirements: Python, Airflow, Spark, Kubernetes, Cloud, Azure, Docker, MLOps Additionally: Sport subscription, Training budget, Private healthcare, International projects.
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