Who are we?

Purpose of the Role

The Opportunity

BTSS Tech has an ambitious mandate: run today’s critical technology with excellence, continuously optimise it, and modernise the technology landscape that will power Sanlam’s future customer experience.

As an AI Solutions Engineer, you will help us identify, build and operationalise practical AI solutions that improve customer journeys, support service employees, simplify processes and strengthen technology operations.

• This is a hands-on junior to intermediate engineering role for someone who has established foundations in software, data or AI engineering and is ready to apply them to real business problems. 
• You will work alongside experienced engineers, architects, product teams and business stakeholders, learning rapidly while taking increasing ownership for solutions from experimentation through to production.
• You will not simply experiment with AI. You will help turn promising ideas into responsible, secure and useful solutions that work in the real world.

Job Purpose:

The AI Solutions Engineer turns emerging AI capabilities into secure, scalable and production-ready solutions that improve customer experiences, empower service teams and help modernise the BTSS Technology landscape. The role works across Generative AI, intelligent automation, data and software engineering.

You will contribute hands-on across the solution lifecycle while building your capability in RAG, agentic AI, cloud engineering and modern software development practices.

What will you do

  1.  Build AI-enabled solutions

• Design and develop components for machine learning, generative AI and intelligent automation solutions.
• Build prototypes and proofs of concept that test whether an AI opportunity is technically feasible, desirable for users and valuable to the business.
• Develop AI-enabled applications such as: 
  o    knowledge assistants;
  o    service-agent copilots;
  o    intelligent search solutions;
  o    document classification and information extraction;
  o    summarisation and response-support tools;
  o    customer-service workflow automation;
  o    technology operations and engineering assistants
• Assist in building agentic workflows that can reason across multiple steps, use approved tools and APIs, and complete controlled actions with appropriate human oversight.

. 2. Engineer solutions for production

• Write clean, modular, testable and maintainable code.
• Package AI capabilities into APIs, services, applications or reusable components that can integrate with BTSS platforms.
• Collaborate with software engineers and architects to connect AI solutions to customer engagement, CRM, service, payments and other enterprise platforms.
• Use version control, code reviews, automated testing and CI/CD practices to improve engineering quality.
• Containerise and deploy solution components using approved cloud and platform services.
• Contribute to the monitoring, support and continuous improvement of AI solutions once they are in production.

3. Work with data and enterprise knowledge

• Prepare, transform and validate structured and unstructured data for use in AI solutions.
• Develop data ingestion and processing components using Java, SQL and relevant data-engineering tools.
• Connect AI solutions safely to approved internal data, documents, APIs and knowledge repositories.
• Help establish traceability between source information and AI-generated outputs.
• Apply data-quality, privacy, security and access-control requirements throughout the solution lifecycle.
• Work with data engineers and platform teams to ensure that solutions use reliable, governed and appropriately authorised data.

4.  Evaluate AI quality and risk

• Develop test cases and evaluation datasets for AI solutions.
  
• Evaluate outputs for relevance, accuracy, groundedness, consistency and usefulness.
  
• Help identify and reduce risks such as hallucination, bias, prompt injection, inappropriate data exposure and unreliable automated actions.
  
• Measure solution performance using agreed technical, operational and user-experience metrics.
  
• Implement logging, monitoring, feedback and human-review mechanisms.
  
• Document known limitations and ensure that AI capabilities are used within clearly defined boundaries.

5.  Solve customer and operational problems

• Work with product owners, service teams and subject-matter experts to understand customer and employee pain points.
• Translate business needs into AI use cases, technical requirements and testable hypotheses.
• Participate in discovery sessions, technical design discussions and solution demonstrations.
• Explain technical choices, limitations and trade-offs clearly to both technical and non-technical stakeholders.
• Use feedback and evidence to improve solutions through short, iterative delivery cycles.

6. Contribute to a modern engineering culture

• Participate actively in code reviews, technical showcases, design discussions and team retrospectives.
• Document reusable patterns, technical decisions and lessons learned.
• Share knowledge with colleagues and contribute to BTSS’s growing AI engineering capability.
• Experiment responsibly with emerging technologies and assess their relevance to BTSS.
• Learn from experienced engineers while progressively taking ownership for more complex solution components.

What success will look like

Within your first 12 months, you will be expected to:

• contribute meaningfully to at least one AI solution that progresses beyond a proof of concept;
• independently develop and test defined solution components;
• demonstrate sound engineering practices in code quality, testing, documentation and version control;
• build a practical understanding of BTSS’s customer, service and technology environments;
• apply responsible AI, security and data-governance requirements in your work;
• work effectively across software, data, product and business teams;
• show clear growth in your ability to take a problem from discovery through to implementation.

Qualifications

A relevant bachelor’s degree, advanced diploma or equivalent practical foundation in one or more of the following areas:

• Computer Science;
  
• Software Engineering;
  
• Data Science;
  
• Information Systems;
  
• Electronic or Computer Engineering;
  
• Mathematics or Statistics;
  
• another relevant technology or quantitative discipline.

Relevant postgraduate study, industry certifications, technical boot camps, open-source contributions or a strong practical project portfolio may be considered as additional evidence of capability.

Experience

This role is suited to an early-career or developing engineer who typically has:

• approximately 2 to 4 years of relevant experience in software engineering, data engineering, machine learning engineering, cloud engineering or AI solution development;
• practical experience developing solutions in Python;
• exposure to taking software, data or AI components beyond a notebook or isolated prototype;
• experience working in a collaborative development environment using Git;
• evidence of practical AI, ML or GenAI work through employment, internships, academic projects, personal projects or open-source contributions.

Candidates are not expected to be experts in every listed technology. We are looking for strong foundations, evidence of practical building, sound engineering judgement and the capacity to learn quickly.

Technical capability

Essential foundations

• Java programming, including readable code, modular design and fundamental object-oriented concepts.
• SQL and a working understanding of relational databases.
• REST APIs, JSON and common system-integration patterns.
• Git and standard version-control workflows.
• Software testing, debugging and technical documentation.
• Foundational understanding of cloud services and cloud-based application deployment.
• Fundamental understanding of machine learning and generative AI concepts.

AI and machine learning exposure

• Large language models and prompt design.
• AI orchestration frameworks such as LangChain, LangGraph, AgentCore, Strands Agents, CrewAI or comparable technologies.
• Frameworks such as Scikit-learn, PyTorch or TensorFlow.
• Evaluation and monitoring of AI-generated outputs.

Cloud and engineering exposure

Experience or practical exposure in some of the following will be advantageous:

• AWS, Microsoft Azure or another leading cloud platform;
• Docker and containerised deployment;
• CI/CD pipelines, including GitHub Actions, GitLab CI, Azure DevOps or equivalent;
• managed AI and machine learning services;
• serverless services and event-driven architectures;
• Infrastructure as Code;
• observability, logging and application monitoring;
• secure application development and DevSecOps practices.

Personal qualities

We are looking for someone who demonstrates:

• Curiosity: You actively explore how new technologies work and where they can create meaningful value.
  
• Builder mindset: You enjoy turning ideas into working solutions, not simply discussing possibilities.
  
• Customer orientation: You remain focused on the customer or employee problem that the technology must solve.
  
• Engineering discipline: You care about quality, security, maintainability and reliability.
  
• Learning agility: You can learn unfamiliar tools and concepts quickly.
  
• Persistence: You troubleshoot methodically and continue iterating when the first approach does not work.
  
• Collaboration: You work constructively with people from different technical and business disciplines.
  
• Clear communication: You can explain your work, assumptions and trade-offs without unnecessary technical complexity.
  
• Responsible judgement: You understand that enterprise AI must be implemented with appropriate controls, transparency and human accountability.

Why join SLS BTSS Tech?

BTSS Tech sits at the intersection of technology transformation and customer experience. The solutions we build influence how clients engage with Sanlam and how effectively our service teams can support them.

You will have the opportunity to:

• apply AI to meaningful customer and operational challenges;
• work across established enterprise platforms and emerging technologies;
• help modernise a complex and important technology landscape;
• develop practical experience across AI, software, cloud, data and integration engineering;
• learn from experienced colleagues while bringing new thinking and energy into the team;
• contribute directly to Sanlam’s Customer North Star and Vision 2030 ambitions.

You will not simply inherit technology. You will help shape what it becomes.

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: 25 November 2026