Machine Learning Engineer

*This is a 12 month contract*Position Title: Machine Learning Engineer (12 month contract)Reports to: Senior Manager, Data & AIJOB PURPOSEThe 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 RESPONSIBILITIESModel Engineering & OptimizationDeploy 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 DevelopmentDesign 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 ManagementImplement 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 & CommunicationWork 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 & DocumentationCreate 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 & EXPERIENCEEducationBachelor’s degree in Computer Science, Engineering, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field (equivalent experience considered).Technical SkillsStrong 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.).