About Our Client Our client is a specialist AI consultancy, partnering with top enterprises to design and implement impactful artificial intelligence solutions. With a reputation for delivering scalable, practical, and high-value projects, they work at the cutting edge of data science and machine learning. Their collaborative, innovation-driven culture offers an environment where you can shape pioneering AI initiatives that influence entire industries. The Role: Senior Machine Learning Engineer This is a 6-month contract opportunity to lead the machine learning delivery of a greenfields proof-of-concept (POC) for one of South Africa’s largest banks, reimagining the loyalty and rewards landscape. You will design and validate models, collaborate with technical and business teams, and ensure that solutions are scalable, robust, and strategically aligned. Key Responsibilities 3 to 5 years of experience in machine learning engineering, applied AI, or data science Design, develop, and validate machine learning models for customer behaviour and rewards optimisation Collaborate with data engineers, solution architects, and client stakeholders to align technical solutions with business objectives Apply strong feature engineering, model evaluation, and reproducibility best practices Explore and test new modelling approaches to deliver measurable outcomes Ensure scalability and robustness with MLOps principles (Bonus) Contribute to operationalising AI solutions from POC to production About You 3 to 5 years of proven experience building and deploying ML models in business contexts Strong proficiency in Python, SQL, and ML libraries (scikit-learn, TensorFlow, PyTorch) Solid understanding of model lifecycle management and cloud platforms (Azure preferred) Excellent problem-solving, communication, and collaboration skills in a consulting environment Experience in financial services, customer analytics, or loyalty/rewards is advantageous Bonus: Hands-on experience in productionising AI solutions Contract Details Duration: 6 months (with potential extension) Location: Johannesburg or Cape Town (hybrid / remote-first flexibility) Start Date: Immediate availability preferred