MLOps Engineer with Python (Senior)

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).

• Python: Advanced

• Cloud (prefered Azure): Advanced

• CI/CD (GitHub Actions / Azure DevOps): Advanced

• IaC: Regular

• Pyspark: Regular

• Airflow: Regular

• MLOps: Proven ability to productionize models and set up monitoring: Regular

• Kubernetes: Nice to have

• 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:

• Solid Python skills, including building production-grade libraries or tools used by other engineers.

• Recent hands-on experience taking ML models to production, including deployment and monitoring.

• Practical understanding of how Spark workloads scale (partitioning, performance tuning).

• Hands-on experience building CI/CD pipelines (GitHub Actions or Azure DevOps) and managing cloud infrastructure as code.

• Cloud experience (Azure preferred, AWS/GCP also welcome) and Docker containerization.

• Practical experience with orchestration tools such as Airflow.

• Smooth collaboration with cross-functional teams and hands-on involvement in technical design.

• Clear and direct communication with both tech and business partners.

• High level of independence, pragmatic problem-solving, and a clear big-picture view.

• English at a minimum B2/C1 level.

• 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

• 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

Tech stack

• Python
• Cloud
• IaC
• PySpark
• Airflow
• MLOps
• Kubernetes
• CI/CD