*This is a 12 month contract*

Position Title: Machine Learning Engineer (12 month contract)

Reports to: Senior Manager, Data & AI

JOB PURPOSE

The Machine Learning Engineer will play a key role in developing and deploying production-grade AI/ML models that support critical business processes such as decision automation, customer analytics, and intelligent operations. The role is responsible for embedding machine learning into scalable, real-time workflows across the organisation.

CORE RESPONSIBILITIES

Model Engineering & Optimization

• Deploy and maintain machine learning models in production environments with strong focus on performance, scalability, and reliability.
• Optimize ML pipelines for low-latency and real-time inference use cases.
• Integrate explainability frameworks (e.g., SHAP) into dashboards and business tools.

Data Pipeline Development

• Design and build scalable ETL/ELT pipelines using Databricks, Python, and SQL for ingesting data from CRM, ERP, and third-party systems.
• Ensure data quality, consistency, and timely availability for ML models and business intelligence platforms.
• Monitor and troubleshoot data pipelines to reduce downtime and support reporting needs.

MLOps & Model Lifecycle Management

• Implement CI/CD pipelines for machine learning using tools such as MLflow, DVC, or SageMaker Pipelines.
• Maintain version control, reproducibility, and consistent deployments across staging and production environments.
• Conduct model validation, A/B testing, drift detection, and ongoing model performance monitoring.

Collaboration & Communication

• Work closely with data scientists to productionize model prototypes for optimal performance and stability.
• Act as the link between technical teams and business stakeholders to integrate ML outputs into daily operations.
• Present insights, findings, and project updates in clear, actionable formats tailored to both technical and non-technical audiences.

Training, Support & Documentation

• Create and maintain documentation for ML models, pipelines, and workflows.
• Provide training to analysts and end-users on interpreting model outputs, risk scores, and key performance indicators.
• Support ad-hoc data requests and contribute to analysis involving integrated ML components.

QUALIFICATIONS & EXPERIENCE

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field (equivalent experience considered).

Technical Skills

• Strong experience across the full machine learning lifecycle including data preprocessing, model development, evaluation, deployment, and monitoring.
• Proficiency in Python, SQL, and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch).
• Hands-on experience with MLOps platforms (MLflow, SageMaker, Azure ML, Databricks Model Serving).
• Familiarity with CI/CD for ML, Docker, and orchestration tools (Airflow, Kubeflow, etc.).