Machine Learning Engineer

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. Collaboration with Data Scientists includes contributing to AI-powered projects while shaping the technical culture.

Project Scope

Deliver price optimisation solutions in close cooperation with a major UK retailer’s data science team. The project enables quick exploration and productionisation of ML models and optimisation algorithms in a hybrid-cloud environment. The objective is to provide 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, 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 and produce features for modelling and analytics.
• Provisioning cloud resources supporting model development in AzureML with infrastructure as code using Terraform.
• Selecting architectural patterns to solve business problems.
• Building robust, maintainable cloud and on-prem code to bring models to production quickly and reliably.
• Establishing a mature DevOps culture and developing reusable solutions for multiple business domains.

Tech Stack

Python (PySpark, Airflow, Azure SDK, FastAPI, MLflow), Spark on Kubernetes, Azure ML, Terraform, GitHub Actions, Docker, Splunk.

Challenges

Address numerous pricing problems in the global retail domain with similar structures and constraints. Build robust, reusable cross-domain solutions using hybrid on-prem and cloud infrastructure, while ensuring high quality and rapid iteration.

Project Environment

Work in a core engineering team of 5–7 professionals, collaborating closely with the client’s product management and data science teams, as well as 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 using the Kubernetes ecosystem.
• Advanced expertise in Azure and Docker.
• Ability to collaborate within an engineering team and contribute to the design process.
• Good English command at B2/C1 level.
• 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.

Passion and willingness to develop matter most; candidates do not need to meet every listed expectation.

Benefits

• Building a tech community.
• Flexible hybrid work model.
• Home office reimbursement.
• Language lessons.
• MyBenefit points.
• Private healthcare.
• Training package.
• Virtusity / in-house training.

Access to the listed perks is optional and completely voluntary for B2B contractors.