Senior AI Engineer

"At Klipboard we've introduced a flexible hybrid work policy, where employees spend three days in the office and two days working from home. This approach promotes a balanced work environment that combines office collaboration with the comfort and convenience of remote work."

Klipboard provides specialist software, services and support to deliver fully integrated trading and business management solutions to companies in the distributive trade - wherever they are in the world. With a unique depth of knowledge and experience in ERP/SaaS solutions, Klipboard has a wide range of clients includes wholesalers, distributors, merchants and retailers from small traders to multinational enterprises. Klipboard has offices in the UK, Ireland, The Netherlands, South Africa, Kenya and North America.

Our mission is simple: to design and deliver high performance, integrated ERP solutions that enable our distributive trade customers to source effectively, stock efficiently, sell profitably and service competitively.

This is a high-impact senior engineering role for someone who wants to shape how a large engineering organisation uses AI in real products and real development work. You will set patterns, create evaluation standards, coach engineers and help Klipboard turn AI from individual experimentation into repeatable, scalable engineering practice.

The role is two jobs in one: the craft of getting reliable, measurable results from large language models through prompt engineering, context design and evaluation; and making everyone around you better h hundreds of engineers across the UK and South Africa, the difference between an organisation where a few people prompt well and one where everyone does is enormous. Your success is measured less by what you personally produce and more by how quickly the engineers around you improve.

Key Activities and Contributions

• Design, build and refine prompts, context strategies and agentic workflows for AI features in Klipboard products, where a confidently wrong price, part match or stock answer is worse than no answer.
• Build evaluation into everything - test prompts against real business cases at scale, measure quality honestly, and treat "it looked fine when I tried it" as the start of the work, not the end.
• Work across models and providers, choosing pragmatically on quality, cost and latency rather than loyalty, and keeping up as models, tools and providers change quickly.
• Get deep into the domain detail with product teams and subject matter experts, because a prompt is only as good as the understanding of the business problem behind it.
• Build and maintain shared prompt patterns, reusable templates, evaluation harnesses and internal guides, so good practice spreads as assets rather than folklore.

Systems, Tools and Technology

• Large language model APIs across multiple providers (GPT, Claude, Llama and equivalents)
• AI coding tools:
  Git Hub Copilot, Cursor or equivalents
• Prompt engineering, context design and agentic workflow frameworks
• Evaluation harnesses, evaluation datasets and automated test suites for AI outputs
• Retrieval-augmented generation (RAG), vector search, embeddings and retrieval pipelines (desirable)
• LLM orchestration frameworks (Lang Chain, Semantic Kernel or equivalents - desirable)

Technical and Professional Expertise

• Strong software engineering background with several years building and shipping production software.
• Substantial hands-on experience with large language models: prompt design, context engineering and structured outputs, in real work rather than personal experiments alone.
• Clear evidence of growing other engineers - through mentoring, teaching, internal training, communities of practice or similar.
• Daily working fluency with AI coding tools such as Git Hub Copilot, Cursor or equivalents, with a practical view of where they help and where they mislead.

Required Qualifications and Experience

• Strong software engineering background with several years building and shipping production software - this role needs an engineer first.
• Substantial hands-on experience with large language models: prompt design, context engineering and structured outputs, in real work rather…