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Machine Learning Engineer

Open 36d

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

Driving business innovation with production-ready machine learning pipelines. The cooperation involves deploying and maintaining ML workflows, leveraging Azure for cloud computing and on-prem clusters for ETLs. Collaborating with Data Scientists includes contributing to AI-powered projects while shaping the technical culture.

Python Expert

Terraform Advanced

Docker Advanced

Azure Advanced

GitHub Actions Advanced

AzureML, Web Apps , AirFlow Nice to have

Spark Nice to have

Dashboarding / visualization skills Nice to have

Exposure to other programming technologies Nice to have

View available projects

Project

Pricing team

Project Scope

Delivering price optimisation solutions in close cooperation with a major UK retailer’s data science team. The project aims to enable quick exploration and productionisation of the ML models and respective optimisation algorithms in a hybrid-cloud environment. The ultimate objective is providing APIs for the optimiser to solve pricing class problems across multiple business domains.

The scope of cooperation covers:

  • Implementing the end-to-end Machine Learning Lifecycle, starting from data preparation through automated deployment to continuous monitoring of data and models in production.

  • Developing PySpark data pipelines to load and transform large amounts of data to produce significant features used in modelling and analytics.

  • Provisioning all cloud resources supporting model development in AzureML with IaC using Terraform

  • Selecting the best architectural patterns to solve business problems.

  • Building robust and maintainable code in the cloud and on-prem to bring models fast and reliably to production

  • Establishing a mature DevOps culture and developing solutions reusable in multiple business domains.

Tech Stack

Python (PySpark, Airflow, Azure SDK, fastapi, mlflow), Spark on K8S, Azure ML and IaC with Terraform, CI/CD with GitHub Actions, Docker, Splunk.

Challenges

Addressing numerous pricing problems in the global retailer world sharing similar structures and constraints. The focus is on building robust solutions reusable across multiple domains, leveraging a hybrid on-prem and cloud infrastructure, and ensuring top quality while maintaining quick iterations.

Project Environment

The project delivery involves a core engineering team of 5-7 professionals in Poland, collaborating closely with the client’s product management, data science team, and internal engineers.

Desired competencies & professional profile:

  • 5+ years of hands-on machine learning engineering experience.

  • Strong proficiency in writing high-quality Python code.

  • Experience with orchestration tools such as Airflow.

  • Knowledge of Spark or other distributed data processing tools.

  • Experience with the Kubernetes ecosystem as a user.

  • Advanced expertise in Azure and Docker.

  • Capability to collaborate within an engineering team and contribute effectively to the design process.

  • Good command of English (B2/C1).

  • Strong communication and mentoring capabilities.

  • Proactive, responsible approach with a strategic, big-picture perspective.

  • Availability to work in a hybrid model from time to time.

Seems like lots of expectations, huh? Don’t worry! You don’t have to meet all the requirements.
What matters most is your passion and willingness to develop. Apply and find out!

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

See also

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