AI Enablement Lead

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.

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, Big Query, 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.

Role

Summary:

AI Enablement Lead
The AI Enablement Lead is responsible for transforming advanced data science into robust, scalable production systems. The role guides a multidisciplinary engineering squad and drives the productionisation of machine learning and LLM models. This includes designing production-grade systems, ensuring engineering and operational excellence, and aligning technical delivery with strategic business objectives.

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

• Drive enterprise-wide improvement in AI delivery velocity and reliability.

• Represent the AI Enablement Squad in strategic forums and contribute to group-wide AI innovation.