Applied AI Engineer - Prompting & Evaluation

Klipboard is a global software company that delivers integrated ERP solutions for the distributive trade. We build C# .NET products that help wholesalers, distributors, merchants and retailers manage sales, inventory and services. We are hiring a Senior Applied AI Engineer to bring AI capabilities into our established codebases and ship production‑ready features quickly.

Key Responsibilities

• 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.

Key Activities and Contributions

• 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.

Systems, Tools and Technology

• 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

Technical and Professional Expertise

• 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.

Core Responsibilities and Contributions

• 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.

Customer Experience

• 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.

Key Outcomes and Activities

• 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.

People, Collaboration & Culture

• 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.

Additional Responsibilities

• 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.

Key Relationships

• 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
• LLM platform and tooling providers (as needed)

Required Qualifications And Experience

• 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.

Preferred Qualifications And Experience

• 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.
• Curiosity about how AI can enhance productivity, decision‑making and customer outcomes, and willingness to learn and adapt as the space evolves.

Equal Opportunities

As a global company, we value and respect the diversity of our workforce, aiming to empower everyone to embrace each other's differences. We are committed to creating an inclusive workplace where diversity, equity, and inclusion are integral to our company and culture. We recognize the benefits of a diverse workforce, where creativity and valuing differences enable us all to thrive and sparks innovation.

Application Support

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