Applied AI Engineer – Prompting & Evaluation

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 including wholesalers, distributors, merchants and retailers from small traders to multinational enterprises. 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.

Klipboard is a global, growing business that embraces AI and emerging technologies to enhance customer outcomes, collaboration, and continuous improvement. We’re looking for people who are curious about or fluid with AI, open to change, and excited to learn how technology can improve the way we work and help our customers which is always supported by strong human insight and communication.

A hands-on building role: taking AI features from idea to shipped, working software quickly, inside real products that real businesses depend on. You design prompts, manage context, integrate models, build evaluations and handle the plumbing and the polish – all of it.

Crucially, most of this work happens in established C# .NET codebases, not greenfield projects. Klipboard's products have been earning their keep for years, and the job is landing modern AI capability inside them cleanly, without breaking what already works. Fast matters here, but fast with evidence – every AI feature needs evaluation behind it before customers see it. We would rather you shipped something measured and honest this sprint than something perfect next quarter.

• Build AI features quickly and properly – from prompts and context design through to full LLM integration in established C# .NET codebases.
• Make them production-grade – error handling, fallbacks, latency management, logging, monitoring and solid evaluation before anything reaches a customer.
• Stay sharp and share as you go – keeping up with a fast-moving space and spreading knowledge through code, examples and conversation.

• Design and build prompts, context strategies and LLM integrations for product features, in domains where a confidently wrong price, part match or stock answer is worse than no answer.
• Work primarily in C# .NET, integrating AI capability into established codebases through clean service boundaries, sensible abstractions and respect for the code that is already there.
• Move fast on real deadlines – prototype in days, harden in weeks, and know the difference between a corner that can be cut and one that cannot.
• Build evaluation alongside the feature, not after it – test against real business cases, measure quality honestly, and let the numbers settle arguments.
• Handle the unglamorous parts well: error handling, fallbacks when a model misbehaves, latency, token cost, logging and monitoring.
• Work with the engineers who own each codebase, fitting in with their patterns and pipelines rather than parachuting in something nobody else can maintain.
• Keep up as models, tools and providers change, and choose pragmatically on quality, cost and latency rather than habit.
• Share what you learn with engineers around you through code, examples and conversation.
• Work with product managers, product owners and subject matter experts to understand the business problem properly, because the best prompt cannot rescue a misunderstood requirement.

• C# .NET (primary development language)
• Large language model APIs across multiple providers
• AI coding tools: GitHub Copilot, Cursor or equivalents
• Prompt engineering and context design patterns
• Retrieval-augmented generation (RAG), vector search, embeddings (desirable)
• Evaluation frameworks and automated quality pipelines for AI outputs

• Solid production experience with C# .NET, including working in established codebases you did not write, and shipping changes into them safely.
• Hands-on experience building with large language models: prompt design, context engineering and structured outputs, in real work rather than tutorials.
• A track record of shipping quickly, with examples of taking something from idea to working software in weeks rather than quarters.
• Experience testing or evaluating LLM outputs in some structured way, and using the results to improve quality.
• Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents.

• Deliver AI features end-to-end: from requirement understanding through to shipped, evaluated product capability.
• Maintain production-grade quality: error handling, fallbacks, latency management, logging and monitoring – an AI feature is production software, with extra ways to fail.
• Care about accuracy, safety and data handling – customers run their businesses on the answers our software gives them.
• Leave things better documented than you found them, so the next engineer can pick up your work without an archaeology project.
• Prototype fast, harden properly, and know the difference between a corner that can be cut and one that cannot.

• Ensure AI features deliver accurate, trustworthy answers – a confidently wrong price, part match or stock answer is worse than no answer at all.
• Understand the business problem behind each feature, not just the technical solution.
• Work with product managers and subject matter experts to ensure AI capability genuinely serves customer needs.

• AI capability shipped into at least one established product with evaluation behind it within the first six months.
• Something built has gone from idea to customers in weeks, and held up in production.
• Evaluation results have changed at least one decision, including, ideally, killing something that was not good enough to ship.
• Engineers around you have picked up techniques from your work, even though teaching is not your primary job.
• You can explain the business problem behind each feature you have built, not just the technical solution.

• Bias to action – would rather build the small version today and learn from it than plan the big version for a month.
• Honest about quality – measures, shows working, and does not ship something they would not stand behind.
• Respectful of existing code and the engineers who maintain it – established systems are established for a reason, and working well within them is a skill you are proud of.
• Curious about the trades Klipboard's customers work in, because domain detail is where the good prompts come from.
• Comfortable with change – the tools will look different in six months, and that suits you fine.

• Leave documentation in better shape than you found it so the next engineer can pick up your work without needing to ask.
• Contribute to shared knowledge and engineering standards as the team's AI practice matures.

• Internal: Product managers and product owners (requirement understanding and feature scoping), Engineering teams who own the established codebases you build into, Subject matter experts in Klipboard's product verticals (distributive trades, rental, automotive), Senior Applied AI Engineer and R&D engineering leadership.
• External: LLM platform and tooling providers (as needed).

• Solid production experience with C# .NET, including working in established codebases you did not write, and shipping changes into them safely.
• Hands-on experience building with large language models: prompt design, context engineering and structured outputs, in real work rather than tutorials.
• A track record of shipping quickly, with examples of taking something from idea to working software in weeks rather than quarters.
• Experience testing or evaluating LLM outputs in some structured way, and using the results to improve quality.
• Daily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents.

• Retrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings.
• Experience with LLM APIs across more than one provider, with a feel for their trade-offs.
• Exposure to any of Klipboard's sectors: distributive trades, rental, retail, automotive aftermarket parts or garage management.
• Experience modernising or extending long-lived systems, in .NET or elsewhere.
• Familiarity with evaluation frameworks, test datasets or automated quality pipelines for AI outputs.

Klipboard is embracing AI at pace across our products and ways of working. We’re looking for people who are curious about how AI can enhance productivity, decision-making and customer outcomes, and who are open to learning and adapting as this space evolves.

• AI capability shipped into at least one established product, with evaluation behind it, and the team that owns that codebase is happy to have you back.
• Something you built has gone from idea to customers in weeks, and held up in production.
• Your evaluation results have changed at least one decision, including, ideally, killing something that was not good enough to ship.
• Engineers around you have picked up techniques from your work, even though teaching is not your primary job.
• You can explain the business problem behind each feature you have built, not just the technical solution.