AI Support Engineer at Ignition Group

Description

Application Development

• Design, build, and maintain features, integrations, and defect fixes across the Group's AI applications.
• Deliver changes through the team's standard development and review workflow.
• Implement changes to a standard that passes senior code review.
• Contribute to technical design discussions for new and existing AI applications.
• Use AI development tools to work efficiently, while remaining accountable for the quality of the output.

Testing and Quality

• Establish and maintain automated testing for AI applications.
• Integrate automated test execution into the release process so that changes are verified before reaching production.
• Establish evaluation and regression testing for AI behaviour, covering prompts, model outputs, and agent workflows.
• Validate model, prompt, and provider changes before release.
• Maintain test cases and test data, and verify defect fixes and releases before they reach users.
• Report test results and quality risks, and raise concerns where the evidence does not support release.

Application Support

• Provide second-line application support, triaging reported issues and establishing their impact.
• Reproduce and diagnose defects, resolving them in code where possible.
• Escalate complex or high-impact problems with clear technical detail.
• Agree service levels and incident severity definitions with each application's business owner, and establish the means to report against them.
• Track recurring issues and feed them into the product backlog.
• Provide practical guidance to users of AI applications.

Monitoring, Documentation and Continuous Improvement

• Monitor the health, reliability, and output quality of AI applications.
• Translate what monitoring shows into prioritised fixes and improvements.
• Maintain technical documentation for supported applications.
• Contribute to improving the team's development, testing, and release practices. • Apply data privacy and security obligations when working with AI applications and the data they process.

Requirements

• Matric / Grade 12 — Required
• Degree or National Diploma in Computer Science, Information Technology, Software Engineering, Infrastructure Engineering, or related technical field — Advantageous

Professional Certifications

• Microsoft, Google, AWS, or AI productivity platform certifications — Advantageous
• Technical support, knowledge management, instructional design, or learning facilitation certifications — Advantageous

Experience 

• 2-4 years' experience in software development, application support, or software testing - Required
• Hands-on experience building or maintaining web applications (for example TypeScript, React or Next.js, and SQL databases) - Required
• Structured software testing experience, including test design and automated testing - Required
• Working knowledge of Git-based development workflows, including branching, pull requests, and code review - Required
• Experience with continuous integration pipelines - Advantageous
• Exposure to AI tools, LLM platforms, or AI-enabled applications - Advantageous
• Experience maintaining technical documentation or operational support resources - Advantageous
• Experience troubleshooting user issues in an operational environment - Advantageous

Skills & Capabilities

• Software development. Able to implement features and defect fixes in a modern web application stack to a standard that passes senior code review.
• Software testing. Able to design test cases and build automated tests for web applications.
• AI evaluation testing. Able to build tests for prompts, model outputs, and agent behaviour, and to interpret the results.
• Troubleshooting. Able to reproduce a reported defect from incomplete information, isolate the cause, and either resolve it or escalate with clear technical detail.
• AI-assisted development. Able to use AI development tools effectively while retaining judgement over the quality of the output.
• Technical communication. Able to explain technical detail clearly to technical and non technical audiences, and to produce usable technical documentation.
• Stakeholder engagement. Able to work productively with users of AI applications and with senior engineers, and to manage expectations on resolution and delivery.
• Adaptability and learning agility in a fast-evolving AI and technology environment.