Job Description

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, 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.
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.

Skills Required