Company Summary
A leading JSE-listed financial services company is committed to improving people’s lives and delivering forward-thinking innovations across the healthcare and financial ecosystem. The organisation thrives on curiosity, high performance, and the pursuit of meaningful change. It offers a dynamic environment where exceptional talent collaborates to create solutions with long-term impact.
About the Group Data Science Team
The Group Data Science Team is expanding and plays a central role in shaping digital, clinical, wellness and behavioural solutions across the business. The team works with large-scale structured and unstructured data on modern cloud and big-data architectures, collaborating with global partners and academic institutions to develop high-impact AI solutions. With a future-fit platform and a focus on new data opportunities, the team builds scalable, production-ready systems that support strategic business priorities. The AI Enablement team is the engineering engine that transforms cutting-edge data science into tangible value for our members and business. We bridge experimental AI and robust, enterprise-scale production systems, enabling scalable, reliable, and efficient AI solutions.
Key Purpose of the role
The AI Lead is responsible for translating cutting-edge data science into robust, scalable production systems. This role leads a multidisciplinary team focused on the productionisation of machine learning and LLM models, ensuring operational excellence, technical innovation, and strategic alignment with business goals. Success in this position requires architecting and implementing production-grade systems that are scalable, maintainable, resilient, and integrated with existing production systems.
Requirements
Essential:
Master’s degree in Computer Science, Engineering, or a related field.
12+ years’ experience in software engineering, data engineering, or AI productionisation.
Advanced proficiency in Python, SQL, cloud-native development, and MLOps/LLMOps tools.
Strong experience with CI/CD, Docker, Kubernetes, and Infrastructure-as-Code.
Advantageous:
Postgraduate qualification in AI, Data Science, or Systems Engineering.
Experience with Vertex AI, BigQuery, Cloud Composer, Kubeflow, or similar platforms.
Attributes:
Collaborative mentor with a passion for developing others.
Pragmatic, delivery-focused, and solutions-driven.
Strong communicator with the ability to simplify complex concepts.
Curious, innovative, and adaptable with an ownership mindset.
Responsibilities
Team Leadership & Delivery
Lead and mentor a cross-functional squad including engineers, developers, analysts, and data scientists.
Drive agile delivery practices to ensure high-quality, on-time deployment of AI solutions.
Technical Strategy & Architecture
Own the technical roadmap for ML and LLM productionisation.
Oversee architectural decisions for model deployment, data pipelines, and cloud-native systems.
Operational Excellence
Implement monitoring, alerting, observability, and incident response processes for production AI systems.
Champion best practices in CI/CD, testing, automation, and reliability engineering.
Stakeholder Collaboration
Work with data science teams to convert prototypes into stable, production-ready applications.
Partner with platform and infrastructure teams to ensure seamless integration and scalability.
Strategic Impact
