Cape Town City Centre: AI Engineer

The AI Engineer plays a critical role in supporting customers with the implementation of MLOps practices, helping organizations streamline their machine learning lifecycle. Success in this role is measured by the ability to guide, enable, and collaborate with customers to ensure the quality, scalability, and continuous improvement of deployed AI solutions, while aligning with business objectives and delivering real-world performance. Define: Develop AI strategies and roadmaps tailored to the customer’s business goals, ensuring a clear path to implementation and value realization. Co-create proposals that incorporate People, Process, and Technology, ensuring alignment with both technical teams and business leaders. Design: Design scalable and secure AI architectures, integrating frameworks, data platforms, and cloud environments (AWS, Azure). Collaborate with internal teams, including Cloud Consultants and Engineers, to ensure alignment between design and delivery phases. Present AI solutions and designs to customers, clearly articulating how they address business objectives and technical requirements. Develop: Design, build, and optimize AI/ML models, including end-to-end workflows for data ingestion, feature engineering, model training, and evaluation. Develop and maintain scalable data pipelines and datasets tailored for model training and validation. Apply advanced Python programming to explore datasets and create innovative AI solutions (e.g. the development of applications). Deploy: Operationalize AI models using MLOps best practices to automate and streamline the machine learning lifecycle. Leverage tools such as Databricks, Azure ML, Kubernetes, and Docker to ensure reliable deployment, scaling, and management of AI solutions. Ensure AI solutions are production-ready, meeting performance, security, and compliance standards. Continuous) Deliver: Monitor and maintain deployed models, incorporating live data and user feedback to improve accuracy and performance. Retrain and optimize models as part of a continuous delivery cycle, ensuring solutions evolve to meet dynamic business needs. Collaborate with stakeholders to align AI solutions with business objectives and ensure measurable outcomes. Collaboration and Quality: Work within Agile teams (Scrum) to deliver AI solutions iteratively, ensuring rapid progress and stakeholder feedback integration. Maintain high standards of code quality, documentation, and automation, fostering best practices across AI engineering teams. Collaborate with AI Consultants, Cloud Engineers, and other technical stakeholders to ensure seamless integration into the broader technology landscape. Required Skills and Expertise Technical Expertise: 2-5 years of experience designing, developing, and implementing AI/ML models. Strong hands-on experience with Databricks , Azure ML , or equivalent cloud-native tools for data exploration, processing, and model development. Proficiency in Python programming and AI/ML frameworks such as TensorFlow , PyTorch , or Scikit-Learn. Practical experience with MLOps tools and practices, including Docker, Kubernetes , and CI/CD pipelines for model deployment and scaling.