AI Engineer

• Hands-on contributor to the design and development of AI-enabled solutions, capable of writing both production-quality code and rapid experimental prototypes
• Develop and implement AI‑enabled microservices, APIs, applications, and internal tools
• Integrate AI capabilities following secure, scalable engineering best practices
• Design, build and validate AI‑driven solutions leveraging providers such as OpenAI and Anthropic
• Enhance low‑code/no‑code automation platforms (e.g., Power Automate, n8n, Workato) by embedding intelligent processing and applying agentic patterns where relevant
• Implement Model Context Protocol (MCP) servers for secure AI‑to‑system connectivity
• Lead AI‑based document parsing and intelligent data extraction initiatives
• Contribute to educating and enabling Enterprise Capabilities areas, including Integration and Automation, by providing guidance, training, and best practices, e.g., on effective use of n8n agents
• Engage with business stakeholders to understand requirements, constraints, and key drivers, identifying and implementing high‑value AI opportunities across Waystone
• Prototype AI features and iterate towards production‑ready capabilities
• Build agentic workflows using frameworks such as LangChain or Microsoft Agent Framework, with a solid understanding of agent fundamentals (tools, memory, orchestration, context control)
• Implement AI agents with tool integration, memory, context control, and guardrails
• Develop retrieval‑augmented workflows to enhance context, reliability, and performance
• Perform quality assurance on AI outputs by implementing robust AI observability practices, including monitoring model behaviour, detecting anomalies, and ensuring visibility into AI performance and reliability
• Contribute to ongoing research and development, staying current with emerging AI tools, frameworks, and techniques to identify opportunities for innovation and improvement
• Apply sound judgment to determine when not to use AI, ensuring traditional deterministic solutions are chosen when they are safer, simpler, or more cost effective
• Ensure AI-enabled solutions consider full total cost of ownership, including token consumption, performance, observability, and ongoing maintenance, with awareness of cost‑efficiency and model‑selection trade‑
• Mentor and support both technical and non‑technical staff (e.g. citizen developers), fostering knowledge sharing and strengthening AI fluency across Waystone
• Act as AI subject matter expert for engineering, testing, architecture teams, as well as business functions across the wider organisation. Deliver demos, internal evangelism, and produce reference documentation
• Nurture the wider internal community, helping uplift AI adoption and responsible, high‑value usage across the business
• Lead the design and documentation of AI-enabled solutions, contributing to Solution In Principle (SIP) or Solution Architecture Design (SAD) documents as needed
• Collaborate with delivery teams throughout the project lifecycles, offering guidance on AI-enabled solutions and addressing technical challenges as they arise
• Develop internal best practices for prompt engineering, AI-processed data handling, AI-assisted coding, creation and sharing of custom agents, and responsible AI usage
• Contribute to AI governance, ethics, compliance, and risk control activities
• Ensure AI solutions comply with enterprise architecture principles, security policies, data governance standards, human-in-the-loop controls, and regulatory requirements
• Monitor and mitigate AI risks, raising concerns early and recommending remedial actions

• Deep understanding of the distinction between Generative AI and Agentic AI, including their foundations, capabilities, and appropriate use cases
• Strong understanding of AI, ML and LLM concepts, including prompt engineering, prompt grounding, iterative loop techniques, context windows, embeddings, RAG, agentic workflows
• Proven ability to integrate AI capabilities both into low-code automation flows and high-code stacks, including, applications, APIs, microservices, distributed systems, and development or testing tools
• Solid software development background with hands-on coding experience in one or more engineering ecosystem such as .NET (C#), Python, or TypeScript
• Excellent communication skills, with the ability to translate complex AI concepts for non‑experts and to effectively influence and collaborate with stakeholders at all levels, both technical and non‑technical
• Strong writing skills, with the ability to contribute to AI literacy and AI fluency documentation
• Strong understanding of responsible AI principles, including governance, bias mitigation, compliance, and risk-based decision-making
• Analytical thinking with excellent problem‑solving ability and keen attention to details
• Ability to mentor developers and testers, and to drive innovation across engineering, QA, and architecture
• Ability to assess AI‑enabled capabilities in third‑party SaaS platforms (e.g., Appian, Salesforce,etc) and provide guidance on responsible, effective adoption
• 5+ years development experience across APIs, integrations, microservices, or full‑stack development
• Demonstrated real‑world experience supported by a portfolio of work that highlights applied skills, solution delivery, and measurable impact, including personal or open‑source AI projects where applicable
• Solid experience integrating AI into workflows and systems across both low‑code and high‑code platforms
• Hands‑on use of AI coding assistants (e.g., GitHub Copilot, Claude Code) and autonomous software engineering agents
• Exposure to RAG, vector databases, embeddings, and AI retrieval systems
• Experience working with cloud AI services and orchestrating AI agents
• Exposure to DevOps practices, CI/CD pipelines, infrastructure-as-code, and cloud platforms such as Azure or AWS in highly regulated enterprise environments
• Extensive experience with source control and version management systems
• Degree in Computer Science, IT, Engineering, or a related discipline (or equivalent practical experience)
• Professional certifications in AI Fluency or specialised AI / ML technologies are advantageous, although strong self‑directed learning and practical AI experience are equally valued

Professional certifications in AI Fluency or specialised AI / ML technologies are advantageous, although strong self‑directed learning and practical AI experience are equally valued