Machine learning engineer for AI solutions

We are looking for an experienced Senior Machine Learning Engineer to join a fast-growing international organisation and take ownership of the development, deployment and continuous improvement of production-ready AI solutions. This position combines hands-on machine learning engineering, MLOps architecture and technical leadership, with a strong focus on delivering scalable AI applications that generate measurable business value.

• Design, develop, deploy and maintain machine learning models throughout their lifecycle, including training, validation, production deployment, monitoring and retraining.
• Lead the development and implementation of applied AI solutions for customer lifetime value prediction, churn modelling, fraud detection and abuse prevention.
• Establish and optimise MLOps frameworks, including automated deployment pipelines, CI/CD processes, environment promotion and model lifecycle management.
• Develop monitoring systems to track model performance, data drift, data quality and measurable business outcomes.
• Define and implement effective model retraining strategies, escalation procedures and continuous improvement processes.
• Apply model explainability techniques, including SHAP and feature attribution, to improve transparency and interpretability.
• Establish best practices for model governance, documentation, version control, reproducibility and auditability.
• Collaborate with Data Engineering teams to develop scalable data pipelines, feature engineering processes and reliable data infrastructure.
• Work with Product, Risk, Commercial and other stakeholders to translate business challenges into practical, scalable AI solutions.
• Analyse production model performance and use monitoring insights to improve model accuracy, reliability and business impact.
• Ensure machine learning solutions meet production standards for stability, scalability, maintainability and operational efficiency.
• Provide technical guidance and mentorship to engineering colleagues, promoting best practices in applied machine learning and MLOps.
• Balance rapid MVP development with long-term production reliability and scalability.

• 5-8+ years of professional experience developing, deploying and maintaining machine learning models in production environments.
• Advanced Python programming skills and strong software engineering fundamentals.
• Solid understanding of machine learning algorithms, model evaluation techniques, feature engineering and predictive modelling.
• Practical experience addressing production ML challenges, including data leakage, model drift, data quality and model stability.
• Hands-on experience with Apache Spark or PySpark for distributed computing and large-scale data processing.
• Experience using MLflow or comparable machine learning lifecycle management platforms.
• Proven experience designing, implementing and maintaining CI/CD pipelines, preferably using GitHub Actions.
• Advanced SQL skills and experience working with large, complex datasets.
• Strong understanding of model deployment, monitoring, drift detection, retraining and production maintenance.
• Experience implementing model explainability and interpretability techniques, including SHAP or similar approaches.
• Demonstrated ability to deliver AI/ML solutions that generate measurable commercial or operational improvements.
• Understanding of model governance, versioning, documentation and reproducibility requirements.
• Ability to design scalable, reliable and maintainable machine learning architectures.
• Strong analytical thinking and problem-solving skills.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Experience collaborating with cross-functional teams, including Data Engineering, Product, Risk and Commercial.
• Ability to balance fast-paced development and MVP delivery with robust production engineering practices.
• Leadership mindset with the ability to mentor colleagues, influence technical decisions and drive engineering best practices.
• Proactive, independent and results-oriented approach to delivering AI solutions.

• Competitive remuneration package based on experience and qualifications.
• Comprehensive employee training and professional development programmes.
• Opportunities to lead high-impact AI and machine learning initiatives.
• Exposure to advanced machine learning technologies and modern MLOps practices.
• International working environment with cross-functional collaboration.
• Multiple company, team and group events.
• Birthday and loyalty benefits.
• Opportunities for professional growth and technical leadership.
• Additional employee benefits and incentives.