Machine Learning Engineer at Sanlam Group

What will you do?

• The successful candidate will be positioned within the AI Solutions team, which is at the forefront of generating data-driven strategies to improve decision-making within the Sanlam Life & Savings Cluster, with the opportunity to expand your impact across other areas of the business.
• You will play a pivotal role at the intersection of cutting-edge DevOps and Machine Learning Engineering. Your primary mission is to operationalize AI and ML models by designing, building, and maintaining scalable, resilient, and efficient ML infrastructure and deployment pipelines.
• You will drive the full AI/ML lifecycle, from experimentation and training to production deployment and monitoring, ensuring that innovative solutions can be delivered at scale, securely, and reliably. You will work closely with AI Solutions Engineers, Data Scientists, Cloud Architects, and business stakeholders to bring transformative AI use cases to life across the organization.
• This role also offers the opportunity to mentor and guide junior engineers, helping to build a high-performing AI/ML engineering practice and fostering a culture of innovation and continuous learning. This is an exceptional opportunity to shape the future of AI deployment in a highly data-centric environment, while gaining deep exposure to both traditional Machine Learning and the rapidly evolving world of Generative AI.

What will make you successful in this role?

Responsibilities will include, amongst others, the following:

• Design, implement, and maintain production-grade ML/AI pipelines using CI/CD best practices.
• Lead the integration of DevOps practices within the ML lifecycle, including version control, containerization, orchestration, and monitoring.
• Deploy and optimize machine learning models in cloud-native environments (e.g., AWS, Azure, GCP).
• Implement MLOps best practices around model versioning, validation, retraining, and monitoring.
• Innovate within the DevOps space to create novel frameworks and automation strategies that spark innovation and position the organization at the forefront of AI operational excellence.
• Collaborate with business development teams and Group Technology SMEs to understand requirements and deliver robust, scalable AI solutions.
• Perform root cause analysis and resolve system issues across both infrastructure and application layers.
• Provide architectural guidance for AI/ML solution design and deployment.
• Enable cross-functional teams through tooling, documentation, and mentoring on scalable AI/ML development.
• Mentor and coach junior engineers and team members, fostering a culture of continuous learning, collaboration, and technical excellence.
• Stay informed about emerging trends and technologies in DevOps, MLOps, and AI deployment strategies.

Qualifications and experience

• Preferred: Master’s degree in Engineering, Computer Science, Statistics, Mathematics, or a related technical field.
• A Bachelor’s degree combined with significant relevant experience will also be considered.
• Certifications in DevOps, Kubernetes, or Cloud platforms (e.g., AWS Certified DevOps Engineer, AWS Certified Solutions Architect, AWS Certified Machine Learning Engineer, Certified Kubernetes Application Developer, Certified Kubernetes Administrator) are highly desirable.

Knowledge and skills

• Strong experience with DevOps tools and practices (CI/CD pipelines, GitOps, infrastructure as code (e.g., Terraform, Cloud Formation, Pulumi etc.)).
• Proficient in containerization and orchestration tools (e.g., Docker, Kubernetes, Amazon ECS, Amazon EKS, Google Kubernetes Engine).
• Deep understanding of networking fundamentals and security principles in cloud-based environments.
• Skilled in designing and maintaining distributed, parallel computing environments (e.g., Spark, Dask, Ray).
• Solid experience with cloud platforms (AWS, Azure, GCP) and their AI/ML toolsets.
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, or similar MLOps platforms.
• Demonstrated ability in debugging complex infrastructure and application issues with strong root cause analysis skills.
• Proven track record of designing and implementing scalable ML/AI architectures.
• Familiarity with model monitoring, data drift detection, and retraining strategies.

Personal attributes

• Analytical and systems-oriented thinker: Able to understand complex ML systems, identify root causes, and design scalable, resilient solutions.
• Collaboration-focused: Works effectively with data scientists, engineers, architects, and business stakeholders to turn ideas into production-ready solutions.
• Adaptable and learning-driven: Comfortable operating in a fast‑evolving AI and Generative AI landscape and committed to continuous learning.
• Strong ownership and accountability: Takes end‑to‑end responsibility for solutions, from experimentation through deployment and monitoring.
• Mentorship-oriented and influential: Enjoys guiding junior engineers, sharing knowledge, and contributing to a strong engineering culture. 

Core competencies

• Cultivates Innovation
• Client Focus
• Drive Results
• Collaborates
• Flexibility and adaptability