Lead AI Engineer – HYBRID (Cape Town), R2.4M – R2.6M

This is an exceptional opportunity for a (very Senior) LEAD ARTIFICIAL INTELLIGENCE ENGINEER to join a team of global AI experts transforming the experience of over 1 million consumers of one of the largest WELLNESS & HEALTH-TECH brands.

Based in CAPE TOWN this LEAD AI ENGINEER role offers a salary of R2.4M – R2.6M.

THE COMPANY:

A much loved and globally recognised leader in the Consumer Wellness and HealthTech market. They blend smart technology and world-class facilities into transformative health and wellbeing experiences for over 1 million consumers.

This business has transformed the way people optimise their health, movement, fitness and recovery.

Their digital innovation journey is now in full-swing, and at the point where AI INNOVATION will result in an even better-quality experience for their >1 million consumers across South Africa, UK, Italy, Australia, Singapore, Thailand, and Qatar.

THE ROLE’S PURPOSE:

This Consumer Business is scaling AI safely and at speed across multiple markets. They’re building a team of AI Engineers to design, build, and run end-to-end AI solutions that improve their consumers’ experience, unlock growth, and drive operational efficiency.

As a Lead AI Engineer you will own one or more domains, lead the most critical initiatives, coordinate the work of others, and raise the bar through technical leadership, coaching, and decision-making.

This role sits between software engineering and data science. You’ll partner with teams across the company (notably Marketing, Digital, Operations, Finance, Legal/Governance) to turn business challenges into working AI solutions. You will be involved end-to-end: from problem framing, to data and modelling, to deployment and monitoring/iterating, with the expectation that you can drive this independently, set direction for others, and define ways of working across the AI lifecycle.

The north-star for the role is: ship AI solutions that consumers appreciate, the team trusts, and that impact the P&L.

THE ROLE:

As a Lead AI Engineer you will:

• Act as a hands-on builder and technical lead for AI-powered products and features, from idea to production, owning the most complex work, making key architectural choices, and ensuring production reliability across teams.
• Combine software engineering, data engineering, and machine learning/LLM skills to deliver solutions, from prototype to production-ready.
• Work closely with business stakeholders to frame problems, define success metrics, and iterate quickly, while leading scope, prioritisation, and trade-offs across multiple initiatives and ensuring you can speak their language.
• Work closely with the Digital and Data teams, as well as with your colleagues in the AI team, acting as the primary technical point of contact for one or more domains (for example CRM and personalisation, operations optimisation, or digital coaching).
• Use the businesses data and AI platforms and selected partners to create reusable components and patterns.
• Own the AI roadmap for at least one domain, in partnership with business and product leaders, ensuring that initiatives are sequenced and resourced to deliver meaningful impact.
• Follow the AI Governance & Privacy framework, ensuring solutions are ethical, compliant, and safe for consumers and colleagues.
• Stay on top of the latest developments in AI, to the extent to which they can benefit your work, present or future.
• Mentor and guide other engineers and data scientists through pairing, code reviews, and technical direction; help define and enforce engineering standards for AI delivery across the team.

REQUIRED BACKGROUND:

• You must have worked in a B2C sector/market – this is essential.
• You may come from a software engineering background with strong ML skills, or from a data science background with strong engineering skills: what matters is that you are already operating at a senior level and are comfortable leading others while owning an AI product’s lifecycle end-to-end.

REQUIRED EXPERIENCE:

• 10+ years in applied data science, machine learning engineering, or software engineering roles with significant AI/ML responsibility, including time spent in a senior or lead capacity.
• Proven track record of delivering end-to-end ML/AI solutions in production, not just notebooks or prototypes.
• Demonstrated experience leading delivery for at least one materially impactful AI/ML initiative (technical leadership, stakeholder alignment, shipping, and operational ownership).
• Experience leading other engineers or data scientists on complex AI initiatives, including planning work, coordinating delivery, and reviewing solutions.
• Experience working in cross-functional teams with Product, Marketing, Operations, or similar business stakeholders.
• Experience in at least one of: customer analytics, personalisation, marketing optimisation, recommendation systems, conversational AI, or operations optimisation.
• Experience shaping a roadmap or portfolio of AI projects in partnership with product and business leaders, not only delivering individual use-cases.

REQUIRED TECHNICAL SKILLS:

• Strong programming skills in Python and SQL. Strong familiarity with Databricks is desirable.
• Strong software engineering fundamentals: system design, testing, code quality, and production troubleshooting for AI services.
• Ability to make system-level architectural decisions for AI services, understanding trade-offs between cost, performance, risk, and speed, and to guide others through those decisions.
• Solid understanding of ML fundamentals: data preparation, feature engineering, model selection, evaluation, and monitoring.
• Hands-on with at least one major ML framework (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow, LightGBM).
• Experience with LLMs and generative AI: prompt design, using APIs, retrieval-augmented generation, evaluation frameworks.
• Comfort with data engineering basics: ETL/ELT pipelines, working with data warehouses/data lakes, job orchestration tools.
• Familiarity with MLOps tooling and practices, e.g. model registries, CI/CD, experiment tracking, containerisation, monitoring.
• Experience with at least one major cloud platform, preferably Azure, but also AWS or GCP, and common data/ML services.
• Comfortable reviewing and approving designs and implementations from other engineers, ensuring alignment with agreed standards and patterns.