AI Solutions Engineer

• Testing solutions and measuring their effectiveness against defined business outcomes

• Taking successful prototypes through to production

• Supporting teams with adoption and new ways of working

• Helping establish reusable AI architecture, engineering patterns and best practices

• Contributing to the organisation's longer-term internal AI and automation roadmap

The Types of Solutions You Might Build

Depending on the opportunities identified, solutions could include:

• AI systems, agents, or automation that perform multi-step business processes

• Automation of current manual processes

• Connecting up distinct systems to improve data flows

• Customer or employee support assistants

• Workflow automation combining AI with existing business systems

• AI and automation capabilities embedded into existing applications

What We're Looking For:

You don't need experience with every technology or use case, but you should have strong experience building real-world software and applying modern AI and automation solutions to practical problems in order to solve business challenges.

Essential

• Strong software engineering experience (use of languages is flexible)

• Hands-on experience building applications using a variety of AI platforms

• Experience building AI agents, tool-calling workflows or deterministic automation

• Experience integrating applications with APIs, databases and external systems

• Ability to understand and analyse business processes

• Strong solution design and architecture skills

• Experience translating ambiguous business problems into practical technical solutions

• Ability to prototype rapidly and iterate based on user feedback

• Strong stakeholder communication and consulting skills

• Comfortable working independently in a greenfield environment

Useful Experience:

Experience with some of the following would be valuable:

• Agent and workflow orchestration frameworks

• RAG, embeddings, vector search and enterprise knowledge retrieval

• Evaluation and monitoring of LLM applications

• Prompt and context engineering

• Cloud platforms such as Azure, AWS or GCP

• Workflow and automation platforms

• Enterprise system integrations

• AI security, permissions, governance and guardrails

• CI/CD and production deployment of AI applications

What Success Looks Like:

Success in this role isn't measured by the number of AI prototypes created. It is measured by whether those solutions improve how the organisation operates.

Examples could include:

• Reducing manual processing time

• Automating repetitive tasks / work

• Improving response or turnaround times

• Increasing operational capacity / efficiency

• Reducing errors or unnecessary hand-offs

• Improving access to organisational knowledge

• Enabling employees to make faster, better-informed decisions

• Moving successful AI ideas from prototype into production

You will probably enjoy this role if you like taking problems that initially sound like "we think AI could help here" and turning them into "here is the redesigned process, here is the working solution, and here is the measurable impact it has delivered."

Working Style

This role requires someone who can operate comfortably across both business and technology.

You should be able to run a discovery session with senior stakeholders, understand how an operational team actually works, challenge assumptions constructively, design an appropriate technical solution and then get hands-on to help build it.

Because this is a greenfield environment, you will need to be comfortable with ambiguity, take ownership and help establish the patterns and foundations that future AI initiatives can build upon.