Lead Automation Engineer

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 and Automation Manager, the Lead Automation Engineer will be vital to the success of Waystone’s engineering, integration, automation and assurance capabilities. The role leads the design, delivery, governance and continuous improvement of automation across Waystone, where the process and the integration are the product: driving efficiency by removing manual and repetitive work, orchestrating workflows, and connecting systems through APIs, webhooks and event-driven integration. Acting as the technical authority for workflow automation platforms — with n8n and Microsoft Power Automate as key strategic technologies — the role sets the standards, reusable patterns and guardrails for building reliable, secure and supportable automations.

The role increasingly draws AI into workflows as a step, such as LLM calls to classify, extract or summarise, working in close partnership with the AI Engineering team, which owns agents, LLM-powered applications, prompt engineering, evaluations and AI safety.

Essential Duties And Responsibilities

• Act as Waystone’s technical lead and subject matter expert for workflow automation and orchestration — with n8n and Microsoft Power Automate as the key strategic platforms — driving efficiency across the organisation by removing manual and repetitive work.
• Define and maintain the automation Centre of Excellence (CoE), including automation standards, reusable patterns, templates, documentation expectations and operational guardrails.
• Lead the design and delivery of complex, enterprise-grade automation solutions integrating SaaS platforms, internal systems, APIs, cloud services and third-party providers.
• Provide hands-on contribution to automation delivery, including workflow design, configuration, scripting, integration logic and technical validation.
• Establish best practice for how automations are designed, tested, deployed, monitored and supported, including error handling, version control, environment separation and observability.
• Embed AI as steps within workflows, such as LLM calls to classify, extract or summarise, consuming capabilities and patterns provided by the AI Engineering team, and preferring deterministic automation where it is safer, simpler or more cost effective.
• Ensure automations that consume AI do so responsibly, operating within the guardrails, evaluation and responsible-AI standards defined by AI Engineering, with appropriate human oversight, auditability, cost control and operational ownership.
• Partner with the Lead AI Engineer and AI Engineering team on hybrid solutions, owning the workflow, integration and orchestration layer while AI Engineering owns agents, LLM-powered features, prompt engineering, model selection and evaluations.
• Support business users, Operations Champions and Power Users in the safe design and adoption of no-code and low-code automations, providing reusable guidance, examples and guardrails.
• Guide the transition of user-developed automations into supported enterprise solutions when they outgrow personal or team productivity use cases.
• Review automation designs to identify fragility, unnecessary complexity, security risks, control gaps and long-term support issues.
• Evaluate emerging automation technologies, including computer-use agents and successors to traditional RPA, and maintain a roadmap for automation capability maturity.
• Uplift automation capability across the business through coaching, knowledge sharing and practical guidance, and promote automation-first thinking, complementing the broader AI literacy and enablement agenda led by AI Engineering.

Requirements

• Deep understanding of low-code automation, workflow orchestration and intelligent process automation, with strong hands-on experience of n8n and Microsoft Power Automate.
• Good understanding of the wider Microsoft Power Platform, and awareness of complementary workflow platforms such as Workato, Xceptor and Appian, with no immediate expectation to use or own them.
• Strong understanding of how to consume AI within workflows, including LLM calls for classification, extraction and summarisation, intelligent document processing and human-in-the-loop design.
• Practical judgement on when to use AI in automation, and when deterministic, rules-based automation is safer, simpler or more cost effective.
• Experience integrating AI services as steps within automation platforms, with sufficient awareness of agents, prompts, guardrails and frameworks such as Microsoft Agent Framework and LangChain to collaborate effectively with AI Engineers.
• Strong understanding of APIs, webhooks, event-driven integration, authentication patterns, data mapping and system-to-system communication.
• Fluency in modern engineering practice, including source control, code review, automated testing, CI/CD and environment management.
• Good understanding of governance, security, privacy, auditability and data handling in a regulated enterprise environment.
• Clear communication across technical and non-technical audiences, with the ability to influence stakeholders and promote best practice without over-engineering.
• The ability to mentor engineers, analysts, Operations Champions, Power Users and business users, uplifting automation capability across the wider organisation.

Experience

• 5+ years’ experience in automation, integration, software engineering, workflow orchestration or a related technical discipline.
• Proven experience designing, leading and delivering enterprise automation using low-code platforms such as n8n or Microsoft Power Automate.
• Experience establishing automation standards, governance and reusable patterns, ideally within a regulated environment.
• Experience embedding AI capabilities as steps within business workflows, delivered in partnership with AI engineering teams.
• Experience collaborating with cross-functional teams and mentoring others in automation, workflow design and platform best practice.

EDUCATION

• Bachelor’s Degree in Computer Science, IT, Engineering, or a related discipline (or equivalent practical experience).
• Relevant certifications in automation, Microsoft Power Platform, software engineering, integration, process improvement, or AI are desirable.