Applied AI Research Engineer

The Applied AI Research Engineer joins our internal applied R&D team, whose mission is to define how AI changes the way software is designed, developed, tested, deployed, and operated across the organization. Rather than academic research or model training, the role centers on hands-on experimentation with emerging AI technologies - agents, LLM integration, evaluation techniques, AI-assisted development - and converting those learnings into the practical engineering standards (the AI SDLC) adopted by our delivery teams.

Responsibilities  

• Explore emerging AI technologies and engineering patterns through hands-on experimentation and rapid prototyping
• Formulate hypotheses, structure experiments, evaluate results against defined criteria, and convert findings into concrete engineering recommendations
• Contribute to the definition and continuous evolution of the organization-wide AI SDLC
• Critically assess AI technologies, model outputs, and industry trends; define evaluation criteria and identify failure modes before recommending broader adoption
• Produce clear documentation, reusable guidance, and recommendations for delivery teams and leadership
• Move fluidly between research, prototyping, engineering, architecture discussions, consulting, and organizational enablement
• Collaborate with the delivery-facing AI rollout function in a continuous feedback loop, incorporating recurring challenges and patterns discovered in real project adoption

Must Have

• Strong software engineering and architecture fundamentals: APIs, distributed systems, testing, delivery practices, and maintainability, transferable across languages and technology stacks
• Hands-on experience with AI agents and agentic workflows
• Experience with LLM APIs and model integration
• Practical experience with evaluation and testing of AI systems
• Awareness of security, privacy, and responsible AI considerations
• Experience with CI/CD and software delivery practices
• Experience with observability and monitoring
• Strong R&D mindset: comfortable operating where established answers do not yet exist, creating structure from ambiguity
• Critical thinking; systems thinking; strong written and verbal communication; pragmatism balancing experimentation with production realities

Nice to Have

• LLM-based application development
• Retrieval-Augmented Generation (RAG)
• Tool/function calling and protocols such as MCP
• API design and integration
• Data pipelines and data management
• Containers and cloud-native development
• Experience with Python, JavaScript/TypeScript, Java, .NET, or similar (specific languages are not a primary selection criterion)

As a people-first organisation, we believe diversity strengthens our teams and drives innovation. All employment decisions are based on merit, skills, and performance, without discriminating based on any personal characteristic. This reinforces our commitment to providing an inclusive and respectful workplace.