At a Glance
- Tasks: Design and refine AI prompts, coach engineers, and uphold evaluation standards.
- Company: Join Klipsboard, a global leader in AI and emerging tech.
- Benefits: Flexible hybrid work, competitive salary, and opportunities for professional growth.
- Other info: Dynamic culture focused on knowledge sharing and continuous improvement.
- Why this job: Make a real impact in AI while collaborating with diverse teams worldwide.
- Qualifications: Strong software engineering background and hands-on experience with large language models.
The predicted salary is between 70000 - 90000 £ per year.
Klipsboard is a global, growing business that embraces AI and emerging technologies to enhance customer outcomes, collaboration, and continuous improvement. The company offers flexible hybrid work, with three days in office and two days remote, across offices in the UK, Ireland, The Netherlands, South Africa, Kenya, and North America.
Key Responsibilities
- Design, build, and refine prompts, context strategies, and agentic workflows for AI features in Klipsboard products.
- Coaching, pairing, and workshops to elevate engineers’ AI capabilities across UK and South Africa teams.
- Define and uphold standards for prompting, evaluation, and context engineering, adapting them as tools change.
Systems, Tools and Technology
- Large language model APIs (GPT, Claude, Llama, etc.)
- AI coding tools (GitHub Copilot, Cursor, and equivalents)
- Prompt engineering, context design, and agentic workflow frameworks
- Evaluation harnesses, datasets, and automated test suites for AI outputs
- Retrieval-augmented generation, vector search, embeddings, and retrieval pipelines (desirable)
- LLM orchestration frameworks (LangChain, Semantic Kernel, etc.) (desirable)
Technical and Professional Expertise
- Strong software engineering background with several years of production software delivery.
- Hands‑on experience with large language models: prompt design, context engineering, structured outputs.
- Experience building evaluation or test harnesses for LLM outputs and using them to improve quality measurably.
- Clear evidence of mentoring or teaching other engineers.
Core Responsibilities And Contributions
- Deliver AI features with proper evaluation and measurable quality standards.
- Embed prompt engineering and evaluation best practice across R&D.
- Maintain high standards for safety, accuracy, and data handling.
- Build and sustain a library of prompt patterns, templates, and guides.
- Deliver coaching tailored to engineers at all maturity levels.
Customer Experience
- Ensure AI features provide accurate, safe, and trustworthy answers.
- Work with product teams and subject matter experts to understand each feature’s business problem.
- Prevent confidently wrong answers such as incorrect prices, part matches, or stock levels.
Key Outcomes and Activities
- Engineers across teams visibly improve due to coaching and knowledge‑sharing.
- At least one AI feature or workflow shipped per quarter with proper evaluation.
- A living, use‑oriented library of prompt patterns and guides.
- A well‑attended community of practice producing reusable outputs.
- Engineering leadership has a clear, evidence‑based view of AI impact.
People, Collaboration & Culture
- Generous with knowledge and energized by others improving.
- Rigorous, honest about results and data.
- Patient across all AI maturity levels.
- Curious about Klipsboard customers’ trades.
- Calm toward rapid model and tooling change.
- Close collaboration with engineering leadership, architecture, product, and cross‑site teams.
Additional Responsibilities
- Travel between UK and South Africa offices for coaching and community building.
- Keep standards current as models, tools, and providers evolve.
- Represent Klipsboard’s engineering practice externally (talks, writing, open‑source).
Key Relationships
- Internal: Engineering leadership and architecture teams; R&D engineering teams across Newcastle, Tankersley, Hungerford, Johannesburg and Cape Town/Stellenbosch; Product managers and owners; Subject matter experts in distributive trades, rental, automotive.
- External: AI and LLM platform and tooling providers (as needed).
Required Qualifications And Experience
- Strong software engineering background with several years of production software delivery.
- Substantial hands‑on experience with LLMs: prompt design, context engineering, structured outputs.
- Experience building evaluation or test harnesses for LLM outputs.
- Clear evidence of mentoring or teaching other engineers.
- Daily fluency with AI coding tools like GitHub Copilot or Cursor.
Preferred Qualifications And Experience
- Experience with retrieval‑augmented generation, agentic workflows, tool use or fine‑tuning in production.
- Experience with LLM APIs, orchestration frameworks, vector search, embeddings, and retrieval pipelines.
- Experience across multiple model providers and understanding of their strengths.
- Domain exposure to distributive trades, rental, retail, automotive aftermarket parts or garage management.
- Experience helping teams adopt AI tooling in legacy codebases.
- Public sharing of work: talks, writing, or open‑source contributions.
What Success In This Role Looks Like
- Engineers identify specific improvements due to coaching.
- At least one AI feature or workflow shipped with reliable evaluation, repeatable by the team.
- New, actively used library of prompt patterns and guides.
- Active, well‑attended community of practice producing reusable outputs.
- Engineering leadership sees clear evidence of AI ROI.
Equal Opportunities
Klipsboard is a global company committed to diversity, equity, and inclusion. We provide reasonable adjustments during the interview and offer processes. We encourage applications from all backgrounds, especially those who may not meet every criterion but bring transferable skills and a passion for the role.
Senior Applied AI Engineer – Prompting & Evaluation employer: Klipboard
Klipsboard is an exceptional employer that champions innovation and collaboration in the AI space, offering a flexible hybrid work model that promotes work-life balance. With a strong focus on employee growth through coaching and knowledge-sharing, team members are empowered to enhance their skills while contributing to impactful AI features. The inclusive culture fosters curiosity and collaboration across diverse teams in the UK and South Africa, making it a rewarding environment for those seeking meaningful work in a rapidly evolving field.
