Senior AI Solutions Engineer

Who Are We?

Sanlam Life and Savings (SLS) is focused on serving our retail and corporate clients in South Africa and further developing our strategic advantages in the South African market. Sanlam Life and Savings consists of the following business units Retail Mass, Corporate, Risk and Savings, Glacier and various business enabling functions. The Sanlam Life and Savings Office provides strategic direction, coordination and support to the four clusters, as well as performing governance oversight that includes assurance provided by second line of defense functions in SLS, to enable us to meet our business objectives.

What Will You Do?

As a Senior AI Solutions Engineer, you 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.

• Key Responsibilities
•    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.

What Will Make You Successful In This Role?

You are passionate about building scalable systems and bringing machine learning models into the real world and not just in notebooks. You are an engineer who thrives in dynamic environments and can bridge the gap between experimentation and production at scale.

• Requirements:
• Technical Expertise:
•    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.

Education & Certifications:
•    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.

• Collaboration & Communication:
•    Excellent communication skills with the ability to translate technical concepts to non-technical stakeholders.
•    Confident in leading discussions and aligning with cross-functional teams, from business development to enterprise architecture.
•    Experience in Agile/Scrum environments and DevSecOps practices.
• Personal Attributes:
•    Innovation Driven - You are curious, self-driven, and thrive in a fast-paced, innovation-focused environment.
•    Persistence - Resilience in troubleshooting issues and iterating on models, understanding that not every approach will yield immediate success.
•    Influential - You demonstrate leadership by influence. This includes mentoring others, setting high standards, and driving initiatives forward.
•    Adaptability - Flexibility to adjust to changing project requirements, tools, and techniques is vital, as the landscape of machine learning is constantly shifting.
•    Accountable - Strong sense of ownership and accountability. You are comfortable leading end-to-end solutions.
•    Analytical - Problem-solver at heart, with a pragmatic and analytical mindset.
•    Forward-Thinking - You stay ahead of the curve—constantly learning, experimenting, and sharing knowledge on the latest in AI, DevOps, and cloud-native technologies.

Turnaround Time

The shortlisting process will only start once the application due date has been reached. The time taken to complete this process will depend on how far you progress and the availability of managers.

Closing date: 13 October 2025

Build A Successful Career With Us

We’re all about building strong, lasting relationships with our employees. We know that you have hopes for your future – your career, your personal development and of achieving great things. We pride ourselves in helping  our employees to realise their worth. Through its five business clusters – Sanlam Fintech, Sanlam Life and Savings, Sanlam Investment Group, Sanlam Allianz, Santam, as well as MiWay and the Group Office – the group provides many opportunities for growth and development.

Our Commitment To Transformation

The Sanlam Group is committed to achieving transformation and embraces diversity.  This commitment is what drives us to achieve a diverse, inclusive and equitable workplace as we believe that these are key components to ensuring a thriving and sustainable business in South Africa.  The Group's Employment Equity plan and targets will be considered as part of the selection process.