Software Engineer; Machine Learning

Job Description About the Role

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.

• 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 necessary.
• Contribute to incident postmortems and help prevent recurrence of issues.
• Understand operational practices - scalability, robustness, fault tolerance, load balancing, and health checking - and apply them to ensure system and model reliability.
• Work effectively with Product Management and Design teams to understand business context and requirements and shape solutions.
• Effectively collaborate with engineering team members to bring out the best in others.
• Communicate complex technical ideas clearly and facilitate team discussions.
• Participate in technical discussions and contribute to team decision-making.
• Proactively improve the team's development, testing, and operational practices.
• Begin mentoring junior engineers and provide constructive feedback.
• Take ownership of tasks and demonstrate leadership in feature delivery.
• Demonstrate growing influence beyond individual tasks to impact the broader team.
• Start scaling through others by guiding team members and delivering through them.
• Relevant degree or diploma in Computer Science, IT, or related field (or equivalent practical experience).
• Typically 5+ years of software engineering experience.
• Proficient in multiple programming languages with understanding of language-specific best practices.
• Comfortable with complex algorithms and optimised data structures.
• Experience with distributed systems, APIs, databases, and scalable architecture.
• Understanding of cloud-based infrastructure and operational practices (monitoring, metrics, fault tolerance).
• Exposure to ML frameworks (scikit-learn, Tensor Flow, PyTorch, or similar).
• Knowledge of cloud services such as AWS VPC, Auto Scaling, serverless computing, storage (EBS, S3), containers, and DNS is preferred, though not a prerequisite.
• Independent Contribution:
  Proven ability to work independently on moderately complex problems, make sound technical decisions, and deliver complete features and ML/data solutions without constant supervision.
• Problem-solving
  
  Skills:
  
  Efficient at debugging and troubleshooting moderately complex issues across code and data/ML systems. Can analyse problems systematically and develop effective solutions with minimal guidance.
• Collaboration & Communication:
  Strong communication skills to facilitate team discussions, work effectively across functions, and explain technical concepts clearly. Builds positive working relationships with Product, Design, and Engineering colleagues.
• Business Awareness:
  Understanding of business context and how…