Fullstack Software Engineer

FACT

Our client in the ICT Sector is currently sourcing Software Engineers to work in Johannesburg to start working as soon as possible.

Longterm Contract: - (initially 6 months, extentable)

70% AI-enabled software delivery | 20% Skills and instruction improvement | 10% architecture and code assurance

Full stack, end-to-end product engineering across frontend, backend, APIs, cloud and data

Digital banking preferred; strong enterprise engineering experience essential

AI-Accelerated Engineering

Deliver enterprise-grade software using AI-assisted and agentic engineering practices.
Use the client's enterprise AI platform alongside tools such as GitHub Copilot and Claude to accelerate implementation.
Direct AI development agents using complete product inputs such as user stories, Figma designs, business rules, design-system components and engineering standards.
Refine AI Skills, markdown instruction files, prompts, workflows and engineering guardrails to improve quality, consistency and delivery velocity.
Build reusable AI-enabled engineering capabilities that reduce repetitive work and improve future delivery cycles.
Evaluate AI-generated outputs critically rather than accepting them at face value.

Full Stack, End-to-End Delivery

Design, build and maintain modern frontend experiences, mobile applications, backend services, APIs, integration services, cloud-native components and persistence layers.
Move between frontend, backend and platform concerns according to delivery priorities.
Work across frameworks and languages rather than being constrained by a single technology ecosystem.
Use established design-system components and engineering patterns to deliver consistent, high-quality experiences.

Contribute to technical decisions within AI-assisted delivery, with support from more senior engineers where needed.
Review architecture and implementation choices proposed by AI agents and flag issues promptly.
Ensure solutions are scalable, maintainable, secure, observable and performant.
Review AI-generated code critically and apply the same standards as for human-written software.
Spot emerging technical debt, architectural drift and quality risks, and raise them early.
Share knowledge and good practices with peers to raise team standards.

Own the engineering quality of AI-generated solutions, including:

Use code review and delivery feedback to improve Skills and instruction files with every iteration.
Translate engineering standards, coding conventions and security requirements into reusable agent guidance.
Identify where AI is effective, where human intervention is needed and where workflows can be refined.
Contribute to establishing operating practices that scale Agentic Engineering across the programme.
Help make this way of working the emerging standard for software engineers.

Senior Software Engineer (4-8 years experience)

Proven experience contributing to the design and delivery of complex, enterprise-grade software products.
Good full stack engineering capability across frontend, backend, APIs, cloud services and data.
Solid understanding of distributed systems, API-driven architectures and modern application architecture.
Experience working effectively in complex environments with evolving requirements and technology choices.
Ability to reason from engineering principles rather than relying exclusively on framework-specific knowledge.
Commercial experience with modern cross-platform mobile engineering.
Experience with React Native and/or Flutter is strongly preferred.
Experience with . NET MAUI, Swift, Kotlin or Ionic is advantageous.
Demonstrated ability to learn unfamiliar frameworks quickly and make sound implementation decisions within them.
Hands-on experience using AI-assisted software development tools such as GitHub Copilot, Claude or equivalent enterprise platforms.
Experience using AI agents to perform software engineering tasks..
Exposure to creating or refining prompts, markdown instruction files or AI-assisted engineering workflows.
Developing understanding of how context, instructions, examples and feedback loops i nfluence agent quality.
Ability to spot obvious AI failure modes such as incorrect assumptions, insecure implementations or superficially plausible but wrong code.