AI Application Development Engineer

At Brennan, we believe that

technology is delivered is every bit as important as

the technology is. We focus on creating real and relevant value for customers with solutions that fit their specific needs and always reflect their true interests.

True performance for our customers starts with a true belief in our people.

It’s why we’ve structured our business to help our teams, and their talents, shine bright. It's why we’ve created a workplace where people of all backgrounds, beliefs and experiences are welcomed and empowered. And it’s why we’ve built an organisation where real innovation makes a genuine impact and generates true rewards for our team members.

In addition to competitive remuneration, Brennan offers extensive benefits, including:

• Training and certification bonuses
• Culture Awards that recognise excellence
• Brennan Daredevils – our annual, all-expenses paid trip awarded to our top performers and outstanding contributors
• Vibrant, fun social activities
• Discounted hardware and software
• An environment that embraces learning and development
• Flexible working arrangement

The AI Application Development Engineer is responsible for building and supporting the AI-assisted applications, modules and skills the Foundry delivers into Brennan and customer environments. The role exists to turn approved ideas into working software quickly and repeatedly, at a consistent standard.

This role focuses on spec-driven development: taking an approved specification through AI-assisted build, peer review, testing and release, and making the result reusable rather than one-off. The Engineer works collaboratively with the Foundry core team, service line representatives, business domains and Architecture to translate business requirements into scalable, secure and maintainable applications.

• Designing, developing, and maintaining AI-assisted applications, Beacon modules, and Cowork skills against approved specifications.
• Building reusable components, patterns, and templates so capability is built once and consumed across multiple service lines.
• Developing and maintaining integrations with REST APIs, Swagger/OpenAPI contracts, Microsoft 365 and MCP connectors, CMDBs, and ITSM platforms.
• Working within the Foundry innovation pipeline — submission, AI review, concept approval, spec approval, build, sprint board — and taking AI-generated build output through to production standard.
• Supporting containerised deployment and Infrastructure-as-Code pipelines using tools such as Docker.
• Contributing to CI/CD pipelines, version control processes, and automated testing practices.
• Assisting in the development of evaluation harnesses, prompt and model regression testing, and output quality measurement for AI features.
• Developing tooling to support telemetry, usage reporting, and outcome measurement for capability once it has shipped.
• Applying the Foundry’s architectural rules and solution patterns, including data classification, data sovereignty, and human-in-the-loop requirements.
• Collaborating with Leaders, Engineers, and service line teams to identify manual processes suitable for AI-assisted automation.
• Participating in code reviews and adhering to established coding standards and governance frameworks.
• Documenting solutions, technical designs, specifications, and operational procedures.
• Assisting in troubleshooting and resolving application defects and incidents through to handover.
• Supporting the enablement of SPECIal Coders and service line builders through pairing, review, and worked examples.
• Contributing to continuous improvement initiatives within the Foundry’s engineering and enablement domains.

• Hands-on experience developing application, integration, or automation solutions.
• Experience building web applications with a framework such as Django, Flask, or similar, backed by a relational database.
• Experience working with REST APIs and integrating with external systems.
• Experience using AI coding assistants and agent tooling as part of day-to-day development.
• Familiarity with LLM application concepts such as prompting, retrieval-augmented generation, agents, or MCP — this can be developed on the job.
• Familiarity with Git and version control workflows.
• Understanding of CI/CD pipelines and automated testing principles.
• Exposure to cloud platforms, preferably Azure, and to containerised deployment.
• Exposure to Linux environments and scripting (Bash or similar).
• Ability to work from a written specification, and to raise gaps rather than assume them.
• Ability to troubleshoot and debug application, integration, and pipeline issues.
• Strong analytical and problem-solving skills.
• Good communication skills with the ability to work collaboratively across technical and non-technical teams.
• Attention to detail with a focus on reliability, security, and maintainability.
• Ability to work independently while contributing effectively within a team environment.