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.
About the role
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 ML-driven forecasting solutions and refine internal engineering standards. The work focuses on classical ML (forecasting, time series, scikit-learn) rather than LLM/GenAI.
Primarily remote-based engagement with occasional on-site collaboration (approx. once a month).
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Python: Advanced
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Cloud (prefered Azure): Advanced
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CI/CD (GitHub Actions / Azure DevOps): Advanced
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IaC: Regular
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Pyspark: Regular
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Airflow: Regular
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MLOps: Proven ability to productionize models and set up monitoring: Regular
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Kubernetes: Nice to have
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English: Advanced
View available projects
Project
Forecasting & Commodities
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.
Key Scope of Services
– 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.
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
What we expect in general:
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Solid Python skills, including building production-grade libraries or tools used by other engineers.
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Recent hands-on experience taking ML models to production, including deployment and monitoring.
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Practical understanding of how Spark workloads scale (partitioning, performance tuning).
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Hands-on experience building CI/CD pipelines (GitHub Actions or Azure DevOps) and managing cloud infrastructure as code.
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Cloud experience (Azure preferred, AWS/GCP also welcome) and Docker containerization.
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Practical experience with orchestration tools such as Airflow.
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Smooth collaboration with cross-functional teams and hands-on involvement in technical design.
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Clear and direct communication with both tech and business partners.
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High level of independence, pragmatic problem-solving, and a clear big-picture view.
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English at a minimum B2/C1 level.
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Primarily remote-based engagement with occasional on-site collaboration (approx. once a month).
Nice to have:
- Experience running workloads, including Spark, on Kubernetes.
A few perks of being with us
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Building tech community
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Flexible hybrid work model
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Home office reimbursement
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Language lessons
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MyBenefit points
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Private healthcare
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Training Package
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Virtusity / in-house training
Access to the above perks is optional and completely voluntary for B2B contractors
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