About Waystone
Waystone is a leading asset-servicing solutions provider of institutional governance, administration, risk and compliance services to financial institutions. With over 25 years’ experience and a comprehensive range of specialist services to its name, Waystone helps our clients structure, operate and grow through our expertise, innovation and digitisation, backed by the operational scale to support global expansion
Summary: Reporting to the AI & Automation Manager, the AI Engineer is responsible for embedding artificial intelligence capabilities into Waystone’s engineering, automation, and assurance ecosystems. Acting as a bridge between cutting‑edge AI technologies and existing high‑code and low‑code platforms, the role focuses on AI enablement rather than foundational model building, ensuring intelligence is thoughtfully integrated into systems and workflows. The AI Engineer designs, develops, and assures AI-enabled solutions, improves automation efficiency, elevates engineering quality, and mentors teams on responsible and effective AI adoption. The mission is to drive innovation, productivity, and intelligent automation across Waystone while upholding compliance, security, and architectural integrity. The role requires strong hands‑on engineering skills, practical understanding of agentic AI patterns, and the ability to guide teams on effective and responsible AI usage.
ESSENTIAL DUTIES AND RESPONSIBILITIES
AI Enablement and Integration:
- 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.
AI Engineering:
- 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‑
Knowledge Sharing, Mentoring and Governance:
- 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.
REQUIREMENTS
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Knowledge, Skills and Abilities:
- 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.
Experience:
- 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.
Education:
- 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.