Design, build, and maintain AI-powered tools, automations, agent orchestration, and integrations that address real business problems identified through the AI enablement pipeline
Translate requirements from the AI Enablement Manager/Specialist and department stakeholders into working technical solutions, scoping feasibility, selecting the right approach, and building to a standard that can be maintained and iterated upon
Assess whether a business problem is best addressed through a custom build, configuration of an existing platform, or adoption of a third-party tool — and make a clear, reasoned recommendation before committing to a delivery approach
Build and maintain LLM-based workflows and AI-assisted tools using the Foods Connected tooling ecosystem, including Claude, n8n, and connected enterprise platforms (HubSpot, Gong, Kantata , and others)
Implement integration patterns between internal systems, AI services, and APIs — applying appropriate authorisation methods and data handling practices at every step
Build with security and IT governance in mind from the outset: all integrations must be visible, documented, and aligned with IT oversight — not shadow infrastructure
Support Work directly with business teams and department heads to understand operational problems, assess what is technically feasible , and explain clearly what can and cannot be done
Support the AI Enablement Manager/Specialist in discovery sessions and workshops, providing the technical perspective needed to turn ideas into actionable requirements
Communicate technical concepts and constraints in plain language — comfortable talking to colleagues who have no engineering background and adjusting accordingly
Contribute to the qualification of inbound AI opportunities from a technical standpoint — assessing integration complexity, data requirements, security implications, and effort
Lead the technical delivery of approved AI initiatives, from initial build through to rollout, user onboarding, and post-implementation iteration
Work alongside Change and Transformation colleagues during rollouts, ensuring that what is built can be understood, used, and supported
Build with observability in mind — instrument solutions so that adoption, usage, and errors can be tracked, and support the monitoring and reporting requirements of the AI Enablement function
Gather and act on feedback from users post-launch, iterating on implementations to improve effectiveness and adoption
Document technical designs, integration architectures, and operational runbooks to ensure continuity and enable handover where needed
Work closely with IT and Security stakeholders to ensure all AI solutions are implemented within sanctioned infrastructure and comply with internal security standards
Apply a security-first mindset to all integration work — handling authentication, data access, API security, and user permissions with appropriate care
Contribute to the AI Governance Committee process from a technical standpoint where relevant — flagging risks, assessing vendor security posture, and supporting technical due diligence on new tools
Maintain documentation of all AI integrations and automations in Confluence, ensuring the organisation has visibility of what is deployed and how it is governed
Support the delivery of internal AI training and knowledge exchange sessions by providing technical depth and practical demonstrations
Help build the technical AI literacy of colleagues across the organisation, able to show people how tools work, not just tell them
Contribute to internal documentation, prompt libraries, and knowledge bases that make it easier for the wider team to work effectively with AI tools
Bring ideas from the broader AI ecosystem, identifying relevant developments, tools, and patterns that could benefit Foods Connected's internal operations
Requirements
Bachelor's degree or equivalent experience in Computer Science, Software Engineering, Information Systems, or a related field
Demonstrable hands-on experience building with LLMs and modern AI tooling — this should be recent, practical experience, not theoretical familiarity
A background that includes working directly with business users, support functions, or customer-facing teams, not purely isolated technical work
Experience building integrations between enterprise systems and APIs, with sound working knowledge of authentication architectures and data flow
Proven ability to take a problem from ambiguous brief to working solution in collaboration with non-technical stakeholders
Hands-on experience with LLM APIs and concepts — including prompt engineering, RAG, function calling, agent frameworks, and tool use. Claude (Anthropic) experience is specifically valued
Working knowledge of MCP (Model Context Protocol) and equivalent integration frameworks — comfortable connecting AI capabilities to enterprise systems in a governed way
Experience with workflow automation and orchestration platforms — n8n experience is directly relevant; equivalent platforms (Zapier, Make, etc.) are acceptable where the underlying concepts are solid
Understanding of web API patterns, authentication methods (OAuth, API keys, JWT), and the security considerations involved in connecting systems
Strong enough engineering fundamentals to build reliable, maintainable solutions in Python or JavaScript/TypeScript — not required to be an expert in both, but solid in at least one
Ability to read and reason about integration architectures, data schemas, and system interactions across a diverse enterprise tooling landscape