{"id":2205883,"url":"https://alion.io/job/klipboard-applied-ai-engineer-prompting-evaluation","title":"Applied AI Engineer – Prompting & Evaluation","company":{"id":2718560,"name":"Klipboard","domain":"klipboard.com","url":"https://alion.io/company/klipboard-com","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"ADP","truth_index":{"grade":"B","score":77,"open_postings":11,"ghost_share":0,"stale_share":0.727,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Johannesburg, South Africa"],"countries":["ZA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"C#","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Agentic Workflows","optional":true},{"name":"Context Engineering","optional":true},{"name":"Function Calling","optional":true},{"name":"Structured Outputs","optional":true}],"status":"live","first_seen_at":"2026-09-07T13:15:00Z","employer_posted_date":"2026-09-07","last_verified_at":"2026-10-23T03:14:08Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"description":"\"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.\"\nKlipboard 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.\nKlipboard 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.\nA 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.\nCrucially, 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.\nKey Responsibilities: \nRole Accountabilities\nBuild AI features quickly and properly - from prompts and context design through to full LLM integration in established C# .NET codebases.\nMake them production-grade - error handling, fallbacks, latency management, logging, monitoring and solid evaluation before anything reaches a customer.\nStay sharp and share as you go - keeping up with a fast-moving space and spreading knowledge through code, examples and conversation.\nKey Activities and Contributions\nDesign 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.\nWork 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.\nMove 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.\nBuild evaluation alongside the feature, not after it - test against real business cases, measure quality honestly, and let the numbers settle arguments.\nHandle the unglamorous parts well: error handling, fallbacks when a model misbehaves, latency, token cost, logging and monitoring.\nWork with the engineers who own each codebase, fitting in with their patterns and pipelines rather than parachuting in something nobody else can maintain.\nKeep up as models, tools and providers change, and choose pragmatically on quality, cost and latency rather than habit.\nShare what you learn with engineers around you through code, examples and conversation.\nWork with product managers, product owners and subject matter experts to understand the business problem properly, because the best prompt cannot rescue a misunderstood requirement.\nSystems, Tools and Technology\nC# .NET (primary development language)\nLarge language model APIs across multiple providers\nAI coding tools: GitHub Copilot, Cursor or equivalents\nPrompt engineering and context design patterns\nRetrieval-augmented generation (RAG), vector search, embeddings (desirable)\nEvaluation frameworks and automated quality pipelines for AI outputs\nTechnical and Professional Expertise\nSolid production experience with C# .NET, including working in established codebases you did not write, and shipping changes into them safely.\nHands-on experience building with large language models: prompt design, context engineering and structured outputs, in real work rather than tutorials.\nA track record of shipping quickly, with examples of taking something from idea to working software in weeks rather than quarters.\nExperience testing or evaluating LLM outputs in some structured way, and using the results to improve quality.\nDaily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents.\nCore Responsibilities and Contributions\nDeliver AI features end-to-end: from requirement understanding through to shipped, evaluated product capability.\nMaintain production-grade quality: error handling, fallbacks, latency management, logging and monitoring - an AI feature is production software, with extra ways to fail.\nCare about accuracy, safety and data handling - customers run their businesses on the answers our software gives them.\nLeave things better documented than you found them, so the next engineer can pick up your work without an archaeology project.\nPrototype fast, harden properly, and know the difference between a corner that can be cut and one that cannot.\nCustomer Experience\nEnsure AI features deliver accurate, trustworthy answers - a confidently wrong price, part match or stock answer is worse than no answer at all.\nUnderstand the business problem behind each feature, not just the technical solution.\nWork with product managers and subject matter experts to ensure AI capability genuinely serves customer needs.\nKey Outcomes and Activities\nAI capability shipped into at least one established product with evaluation behind it within the first six months.\nSomething built has gone from idea to customers in weeks, and held up in production.\nEvaluation results have changed at least one decision, including, ideally, killing something that was not good enough to ship.\nEngineers around you have picked up techniques from your work, even though teaching is not your primary job.\nYou can explain the business problem behind each feature you have built, not just the technical solution.\nPeople, Collaboration & Culture\nBias to action - would rather build the small version today and learn from it than plan the big version for a month.\nHonest about quality - measures, shows working, and does not ship something they would not stand behind.\nRespectful 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.\nCurious about the trades Klipboard's customers work in, because domain detail is where the good prompts come from.\nComfortable with change - the tools will look different in six months, and that suits you fine.\nAdditional Responsibilities (as aligned to company job architecture)\nLeave documentation in better shape than you found it so the next engineer can pick up your work without needing to ask.\nContribute to shared knowledge and engineering standards as the team's AI practice matures.\nKey Relationships (Internal and external)\nInternal\nProduct managers and product owners (requirement understanding and feature scoping)\nEngineering teams who own the established codebases you build into\nSubject matter experts in Klipboard's product verticals (distributive trades, rental, automotive)\nSenior Applied AI Engineer and R&D engineering leadership\nExternal\nLLM platform and tooling providers (as needed)\nRequired Qualifications and Experience\nSolid production experience with C# .NET, including working in established codebases you did not write, and shipping changes into them safely.\nHands-on experience building with large language models: prompt design, context engineering and structured outputs, in real work rather than tutorials.\nA track record of shipping quickly, with examples of taking something from idea to working software in weeks rather than quarters.\nExperience testing or evaluating LLM outputs in some structured way, and using the results to improve quality.\nDaily fluency with AI coding tools such as GitHub Copilot, Cursor or equivalents.\nPreferred Qualifications and Experience\nRetrieval-augmented generation, agentic workflows, tool use, vector search or embeddings in production settings.\nExperience with LLM APIs across more than one provider, with a feel for their trade-offs.\nExposure to any of Klipboard's sectors: distributive trades, rental, retail, automotive aftermarket parts or garage management.\nExperience modernising or extending long-lived systems, in .NET or elsewhere.\nFamiliarity with evaluation frameworks, test datasets or automated quality pipelines for AI outputs.\nKlipboard 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.\nWhat Success in This Role Looks Like\nAI 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.\nSomething you built has gone from idea to customers in weeks, and held up in production.\nYour evaluation results have changed at least one decision, including, ideally, killing something that was not good enough to ship.\nEngineers around you have picked up techniques from your work, even though teaching is not your primary job.\nYou can explain the business problem behind each feature you have built, not just the technical solution.\nCompany Info\nYou may also have seen from our recent posts that we are excited to begin sharing our new company name - Klipboard. Kerridge Commercial Systems (KCS) is becoming Klipboard and our new brand is designed to bring together our expertise across distribution, automotive, retail, rental, transport management, manufacturing, and field service management. We have offices based across the world and we are looking for talented individuals to join our growing teams. Due to our growth over the last few years it is an exciting time to join us as we enter our next chapter! 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.\"\nEqual Opportunities\nAs 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.\nIf you require any help, adjustments and/or support during the interview and offer process then please advise our TA or HR team.\nResearch shows that women and other underrepresented groups are less likely to apply for a role unless they meet every listed requirement. However, we recognise that skills and experience come in many forms, and we encourage you to apply even if you don’t meet every criterion. If you are passionate about this role and believe you have the right mindset and transferrable skills, we would love to hear from you!\nTo all recruitment agencies: Klipboard does not accept agency speculative resumes. 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