Associate AI Solutions Engineer

What will you do?

You will join the AI Solutions team, at the forefront of building and deploying data-driven and GenAI-powered solutions across the Sanlam Life & Savings Cluster.
This is a hybrid engineering role, part ML Engineer, part DevOps Engineer, part AI Engineer. You'll help build machine learning and GenAI capabilities and package them into reliable, production-ready pipelines running on AWS. You'll work closely with AI Solutions Engineers, Data Scientists and cross-functional teams, gaining hands-on exposure across the full ML/AI lifecycle, from feature engineering to cloud deployment. You'll work closely with the wider AI Solutions team, Data Scientists, and cross-functional colleagues as you build breadth across ML, GenAI, and cloud engineering. You won't be expected to have deep expertise in all these areas from the start, this is a role designed for hands-on learning, with a structured technical growth framework offering a clear path to Intermediate and Senior levels as your capability develops. This is a fantastic opportunity for someone early in their engineering career who wants to build strong foundations in modern AI infrastructure.

What will make you successful in this role?

Key Responsibilities:

Data & Model Foundations: Assist in data preprocessing, feature engineering and contributing to shared feature stores. Support the building, tuning and evaluation of ML models using frameworks such as Scikit-learn and PyTorch.
GenAI & LLM Systems: Gain exposure to LLM fundamentals, prompt engineering and Retrieval-Augmented Generation (RAG) patterns, including working with embeddings and vector search, with support from the wider team.
MLOps & Deployment: Support the build and maintenance of ML pipelines, training, deployment and monitoring, helping models move from experimentation into production and tracking basic performance and drift metrics.
Cloud & DevOps: Work with AWS core services (S3, EC2, Lambda, IAM) and containerization tools (Docker) to support deployment. Contribute to CI/CD pipelines and version-controlled workflows (Git) and get exposure to Infrastructure-as-Code concepts.
Code Quality & Collaboration: Write clean, testable code, participate in code reviews and help establish good MLOps hygiene. Communicate progress and technical trade-offs clearly with the team.
Continuous Learning: Stay curious. Proactively build knowledge of new tools and trends across ML, GenAI, and cloud engineering.

Qualifications and experience

- Bachelor’s degree in Engineering, Computer Science, Statistics, Mathematics, or a related field.
- Foundational programming experience in Python (OOP, readable code)
- Exposure to SQL, R or Java is advantageous.
- Basic exposure to containerisation (Docker) and CI/CD tools, such as GitHub Actions or GitLab CI, is advantageous.

Knowledge and skills

- Basic understanding of databases and database querying.
- Awareness of AWS (or another cloud provider) core services, such as S3, EC2, Lambda and IAM.
- Familiarity with Git and standard version-control workflows.
- Exposure to ML frameworks (Scikit-learn and PyTorch) and basic hyperparameter-tuning concepts.
- Conceptual understanding of LLMs, prompt engineering and RAG architectures is advantageous.
- Strong analytical and problem-solving skills with attention to detail.
- Excellent written and verbal communication skills, with the ability to engage confidently with technical and non-technical stakeholders.
- A collaborative and team-oriented approach to work.

Personal attributes

Curiosity: A strong desire to learn and stay updated with the latest trends and advancements in machine learning and AI.
Creativity: Innovative thinking to develop unique solutions and approaches to complex challenges.
Persistence: Resilience in troubleshooting issues and iterating on models, understanding that not every approach will yield immediate success.
Adaptability: Comfort with shifting tools, priorities and techniques in a fast-moving field.
Pipeline driven mindset: Ability to connect the technical solution dots to achieving a desired end-state.

Core competencies

Cultivates Innovation
Client Focus
Drive Results
Collaborates
Flexibility and adaptability