Technical Lead - AI Enablement Squad

Imagine a world where people live healthier, more enhanced and protected lives… A world in which each organisation is a powerful influencer and responsible corporate citizen, committed to being a force for social good. As a leading innovator in healthcare, wellness, insurance, investments, financial and life planning, Discovery works ceaselessly to make this vision a reality. Fuelled by our passion for enhancing lives and our desire to innovate, Discovery consistently sets global standards, creating shared value through shared intellectual capital. A testament to this is our Vitality programme, which is both a platform to incentivise people to live healthier lives as well as a channel through which shared value is delivered. We are a proudly South African-born, global company with health, life and short term insurance operations in South Africa and the United Kingdom, and a presence in Germany, France, the United States, Canada, Australia, Singapore, Hong Kong, Philippines, Thailand, Malaysia, China and Japan through our Global Vitality Network. One way in which we uphold our promise of shared value is by being a positive disruptor that focusses on bringing about sustainable change in the lives of the people and communities we serve across the globe. Our values of leadership, honesty, innovation and fairness act as our compass, directing our business practices to take advantage of every opportunity while looking for ways to dazzle clients. Key Purpose The AI Enablement Squad Technical Lead is a senior hands‑on leader responsible for translating cutting‑edge data science into robust, scalable, production‑grade AI systems. The role provides deep technical leadership across the productionisation of machine learning and LLM solutions, with a strong emphasis on advanced Python engineering, sound software and systems architecture, and engineering excellence. This role is accountable for technical design, implementation quality, and architectural integrity across AI-enabled systems, working closely with data scientists, engineers, and platform teams. The role acts as a technical authority and mentor, guiding engineering decisions, reviewing critical code, and supporting the growth of junior and mid-level technical team members. The position involves hands-on ownership of the end-to-end technical lifecycle—from system and data pipeline design, through build, testing, deployment, and production monitoring. Success in this role requires architecting and implementing maintainable, resilient, and scalable systems, integrating effectively with existing enterprise platforms, and ensuring alignment with Group development, security, and governance standards. Key outputs The successful applicant will be responsible for but not limited to the following job functions: Areas of responsibility may include but not limited to Hands on Technical Leadership & Mentorship Act as a senior hands-on technical lead and principal engineer, contributing directly to critical production Python codebases. Provide technical mentorship and guidance to junior and mid-level engineers through code reviews, pairing, and architectural discussions. Set and uphold high standards for code quality, testing, reliability, and maintainability, fostering a culture of engineering excellence. Production Python & Systems Architecture Lead the technical design and implementation of production-grade AI systems, with a strong emphasis on advanced Python engineering. Design and influence system and application architecture for model deployment, inference services, data pipelines, and integrations. Make pragmatic architectural decisions balancing scalability, resilience, performance, cost, and time-to-value, in alignment with Discovery's enterprise and security standards. MLOps / LLMOps & Operational Excellence Design, implement, and evolve production ML and LLM workflows, including deployment, monitoring, and lifecycle management. Champion best practices in CI/CD, automated testing, observability, and incident analysis for AI-enabled systems. Ensure AI systems are observable, resilient, and supportable in production environments. Cross functional Technical Collaboration Work closely with data scientists to translate experimental and research work into production-ready systems. Collaborate with platform and infrastructure teams to ensure solutions integrate cleanly with existing enterprise platforms. Provide clear technical input on feasibility, constraints, and trade-offs to business and technical stakeholders. Technical Influence & Enablement Influence the technical direction and sustainability of AI delivery across the organisation through shared patterns, tooling, and architectural guidance. Drive the adoption of agreed engineering standards, platforms, and best practices for AI productionisation. Represent the AI Enablement Squad as a technical authority in architecture and engineering forums, contributing to group-wide technical capability uplift. Personal Attributes The successful candidate would need to have the following competencies: Collaborative mentor with a natural inclination to share knowledge. Pragmatic and results-driven, focused on delivering robust solutions. Intellectually curious with a passion for technology and innovation. Excellent communicator, able to articulate complex technical ideas clearly. Ownership mindset with resilience and adaptability. Skills Levels of proficiency in Python, SQL and cloud-native development to enable management of the delivery team Similarly, for MLOps/LLMOps tools (e.g. MLflow, Kubeflow, LangChain, etc.), as well as CI/CD, containerisation (Docker, Kubernetes), and infrastructure-as-code Advanced knowledge of cloud platforms (either GCP, AWS or Azure) Agile delivery Education and Experience The following requirements are Essential: Degree in Computer Science, Engineering, or related field 6+ years in software/data engineering or AI productionisation 8+ years, with leadership experience in cross-functional technical teams The following requirements are advantageous: Postgraduate qualification in AI, Data Science, or Systems Engineering