Technical ML Lead

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.

• 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.

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

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.

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.