Full Stack Engineer - AI (Public Sector)
Summary
Full-stack engineer building AI-powered applications and developer tools for government clients, working across React.js, TypeScript, Node.js, and LLM workflows with direct customer-facing technical delivery.
We are looking for a hands-on Software Engineer with strong AI engineering capabilities who can build production AI systems and work directly with the teams using them. This role spans full-stack development, LLM applications, evaluation, observability, deployment, and customer-facing technical delivery. Accessibility experience is not required, but you should be willing to develop expertise in digital accessibility and WCAG standards.
Responsibility
- Design, develop, and maintain high-quality features and services for government product teams, ensuring accessibility and performance.
- Develop full-stack features across frontend, backend, APIs, data, and AI systems.
- Design and build production-grade applied AI-powered applications and developer tools.
- Utilise CI/CD pipelines to ensure seamless, reliable, and high-quality deployments.
- Develop and maintain technical documentation, including API specs, troubleshooting guides, and system architecture diagrams.
- Translate customer feedback and production issues into reusable product improvements.
- Work alongside Product Management to provide L1/L2 support to customers we serve.
- Work directly with government teams on discovery, demos, Proof-of-Concepts, architecture discussions, onboarding, integrations, and troubleshooting.
- Gain knowledge debugging and remediating WCAG accessibility issues on production websites
- Proven experience in designing and building scalable, reliable, and maintainable software systems.
- Strong proficiency in modern software development (e.g., React.js, TypeScript, Node.js) and a deep understanding of browser architecture.
- Build LLM workflows using RAG, tool calling, structured outputs, agents, and human-in-the-loop patterns.
- Build evaluation pipelines using golden datasets, regression tests, deterministic evaluators, and LLM-as-a-judge.
- Implement observability, monitoring, guardrails, fallbacks, and validation for reliable AI systems.
- Balance model quality, latency, reliability, maintainability, and cost.
- Maintain automated tests, CI/CD pipelines, and technical documentation.
- Hands-on experience with quality engineering practices, including automated testing (unit, integration, E2E) and test automation frameworks.
- Solid grasp of CI/CD, infrastructure concepts, and agile engineering practices.
- Strong troubleshooting and debugging skills, with an ability to analyze complex system behaviors and logs.
- Excellent communication skills to explain technical trade-offs and quality standards to diverse stakeholders.
Nice to Haves
- Docker, Infrastructure as a Code and Bash scripting knowledge
- Passion for building inclusive technology and a willingness to apply or develop expertise in WCAG 2.2 standards and assistive technologies.
- Willingness to learn knowledge of digital accessibility, including digital accessibility regulations & Web Content Accessibility Guidelines (WCAG) 2.2 Level A and AA requirements, learn how to use screen readers such as VoiceOver, NVDA and TalkBack.
- Experience writing code that meets accessibility standards such as the Web Content Accessibility Guidelines (WCAG).
- Candidates with more than 3 years of relevant experience in software engineering may be considered for senior positions.