Position: Machine Learning Engineer

Job Type: Full-Time Position

Job Start: Immediately

Location: Flexible (IRE, UK)

Reports to: Head of Data & Emerging Technology

Additional Benefits: Laptop & smartphone will be provided. Any relevant training & development courses will be company funded.

Role Overview

As a Machine Learning Engineer, you will design and deliver ML solutions that leverage enterprise data within our Microsoft Fabric platform. You will focus on building scalable data pipelines, training predictive models, and embedding these into business processes and reporting to drive smarter decision-making across operations, finance, and shared services.

Working as part of the Data & Emerging Technologies team, you will take ownership of the end-to-end lifecycle of ML use cases — from problem definition and data preparation through to deployment and monitoring.

This is a hands-on role, suited to someone who is comfortable working directly with data at scale, building models, and embedding them into real business processes rather than just experimentation.

Key Responsibilities

• ML Solution Delivery: Design and build ML solutions using enterprise data within Microsoft Fabric, focusing on practical use cases such as forecasting, anomaly detection, and optimisation.

• Data Preparation and Feature Engineering: Work with large-scale structured data (e.g. ERP and operational systems) to prepare, transform, and engineer features suitable for modelling.

• Model Development and Evaluation: Build, test, and refine ML models, ensuring outputs are accurate, explainable, and aligned to business needs.

• Pipeline Development & Integration: Develop and maintain ML pipelines within Fabric (Lakehouse, notebooks, pipelines), integrating outputs into downstream tools such as Power BI and operational workflows.

• Model Deployment and Monitoring: Deploy models into production, ensuring they are scalable, monitored, and maintained with appropriate retraining and performance tracking.

• Data Platform Alignment: Collaborate with the Data Enginner to ensure all ML solutions are built on governed, high-quality datasets within the OneLake/Fabric ecosystem.

• Cross-Team Collaboration: Work closely with IT, data, and business teams to identify opportunities, validate use cases, and deliver solutions that drive measurable impact.

  
  

Skills & Experience

Experience: 2–5 years’ experience working with machine learning, data science, or data engineering in a production environment. Experience delivering solutions using large-scale, structured enterprise data.

Technical Skills:

• Hands-on experience with machine learning and data tools, ideally within the Microsoft ecosystem (e.g. Python, PySpark, Microsoft Fabric, or similar data platforms).

• Strong understanding of building and evaluating ML models (e.g. regression, classification, forecasting) using structured enterprise data.

• Experience working with large datasets, including data preparation, transformation, and feature engineering (e.g. SQL, Spark, or similar).

• Familiarity with developing and managing data and ML pipelines, including batch processing and integration into downstream systems.

• Understanding of ML lifecycle practices including deployment, monitoring, performance tracking, and model versioning.

• Awareness of how machine learning outputs can be integrated into reporting and operational tools (e.g. Power BI, business systems).

  
  

Soft Skills:

• Strong collaboration and communication skills, including ability to work with both technical and non-technical teams.

• A practical mindset — focused on delivering usable, reliable, and ethical automation in real-world business settings.

• Curiosity to stay on top of Microsoft’s evolving AI capabilities and identify valuable use cases as the landscape matures.