Software Engineer (Machine Learning

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Sanlam was established as a life insurance company in South Africa but has since transformed into a diversified financial services group that operates across the African continent, India, Malaysia and selected developed markets, with listings on the Johannesburg, A2X and Namibian Securities Stock exchanges. In 2018 the Group celebrated its centenary as well as 20 years since demutualisation and listing in South Africa and Namibia.

Sanlam is one of the largest internationally active insurance groups in the world with a presence in 27 countries and has the biggest non-banking financial services footprint on the African continent.

The Group's four business clusters (Sanlam Life and Savings, Sanlam Investment Group, Sanlam Allianz and Santam) house the Group's business operations. The Group Office provides strategic direction and support to the four clusters, assisting them in realising their strategies and meeting their business objectives. The Group Office is responsible for governance and for the Group's centralised functions, which include:
Group Tecnhnology, Finance, Actuarial, Risk and Balance Sheet Management, Strategy, Human Capital, Brand, Marketing and Corporate Affairs. The Group Office ensures cohesive management across the organisation.

SFTx is a newly established digital first business unit within the Sanlam Group on a mission to democratize financial advice and solutions for everyone across the African continent. We exist to pioneer inclusive financial confidence helping people build strong foundations to bridge the gap in generational wealth. Our culture is that of agility and constant deployment, we believe in learning fast, learning cheap and learning forward.

Our aim is to provide a work environment where knowledge workers can accelerate the development of their ideas and bring innovation to market, at the same time provide compelling career and development proposition that will enable them to realize their dreams.

A Software Engineer (ML) is an independent contributor who takes ownership of ML-driven and data-intensive features from concept to production, whilst mentoring junior engineers and actively improving team practices. This role requires the ability to work autonomously, solve problems across the codebase and data/ML stack, and collaborate effectively with cross-functional teams. SEs in this track deliver complete features and models independently whilst demonstrating growing influence beyond individual tasks to impact the broader team.

Reporting to a Tech Lead, this role requires solid technical expertise, proven ability to deliver end-to-end solutions across data and ML systems, and emerging leadership skills in mentoring and process improvement. You will work closely with Product Management and Design teams to understand business context, collaborate with engineering peers to deliver high-quality solutions, and actively participate in improving development, testing, and operational practices across the team.

• Take full ownership of new features and ML/data products, delivering them from concept to production independently.
• Work independently to create solutions without constant direction or supervision.
• Design and implement moderately complex systems, including ML pipelines and data workflows, with understanding of trade-offs.
• Solve problems in any part of the codebase, including data and ML components, demonstrating breadth of technical capability.
• Balance feature delivery with long-term code quality and technical considerations.
• Proactively identify and address small pieces of technical debt within work scope.

• Focus on operational and code excellence during code reviews and feature development.
• Contribute to coding standards and ensure code efficiency and reusability.
• Write robust, well-tested code that meets team quality standards.
• Participate actively in code reviews, providing constructive feedback to peers.
• Apply understanding of design principles and software engineering best practices.
• Work with multiple frameworks and explore libraries as needed for solutions, including ML frameworks such as scikit-learn, Tensor Flow, or PyTorch.
• Apply understanding of the ML model lifecycle: training, evaluation, deployment, and monitoring.
• Design and maintain data pipeline orchestration and workflow management.
• Apply data versioning, feature stores, and ML metadata management practices.
• Build and maintain model serving patterns and APIs for ML systems.
• Articulate the pros and cons of relevant data structures and algorithms for time and space complexity, including trade-offs as scope evolves.

• Participate actively in on-call rotations and handle incidents effectively.
• Implement robust monitoring, logging, and alerting for owned features, services, and ML systems.
• Respond to production issues promptly and elevate appropriately when…