Senior AI Developer

Apache Associates; Sheffield; On-site; GBP 90,000 - 120,000; Full time

Job summary

Apache Associates in the United Kingdom is seeking a Senior Applied AI Engineer to lead production AI initiatives, turning capabilities into robust, production-ready solutions. You will design prompts, context strategies and agentic workflows while shaping evaluation practices across the engineering org.

You’ll work with engineers, product teams and SMEs to identify where AI adds value and where it shouldn’t.

Qualifications

• Several years of experience building and shipping production software.
• Significant hands-on experience with Large Language Models in commercial environments.
• Strong prompt engineering, context engineering and structured outputs.
• Experience building evaluation frameworks, test harnesses or datasets for LLM outputs.
• Evidence of using evaluation and data to measurably improve AI quality.
• Proven ability to mentor, coach and develop other engineers.
• Experience delivering internal training, workshops or communities of practice.
• Daily experience with AI development tools such as GitHub Copilot, Cursor or equivalents.
• Pragmatic understanding of AI coding tools’ strengths and limits.
• Confidence to challenge assumptions and use evidence over hype.

Responsibilities

• Design, build and refine prompts, context strategies and agentic workflows for real-world AI product features.
• Develop robust approaches to evaluating LLM outputs, using real business cases and measurable quality standards.
• Work across multiple LLM providers and models, making pragmatic decisions around quality, cost and latency.
• Collaborate with Product Managers, Product Owners and subject matter experts to understand business problems before designing AI solutions.
• Coach and mentor engineers across multiple teams, helping them get more value from LLMs and AI coding tools.
• Run pairing sessions, technical reviews, workshops and one-to-one coaching.
• Build and maintain reusable prompt patterns, templates, evaluation harnesses and internal guidance.
• Establish and lead an internal AI community of practice for sharing successes and lessons learned.
• Define what good looks like for prompting, context engineering and evaluation across R&D.
• Keep standards current as models, providers and AI tooling evolve.
• Provide engineering leadership with measurable evidence of AI value.
• Act as a constructive challenger when AI demos are not robust enough for production.
• Promote high standards around accuracy, safety and data handling.

Skills

Production software

Large Language Models

Prompt engineering

Context engineering

Structured outputs

Evaluation frameworks

Mentoring

AI development tools

GitHub Copilot

Cursor

RAG

Agentic workflows

Fine-tuning

Vector search

LangChain

Semantic Kernel

Tools

GitHub Copilot

Cursor

LangChain

Semantic Kernel

Job description

Apache Associates are working with an ambitious SaaS organisation that is investing heavily in AI across its products and engineering function. We’re looking for aSenior Applied AI Engineer to take a leading role in turning AI capabilities into genuinely useful, production-ready solutions.

This is a senior, hands-on position for someone who is as passionate about building with AI as they are about helping other engineers get better at it.

You’ll be responsible for designing and refining prompts, context strategies and agentic workflows, while also establishing best practice around evaluation and AI-assisted development across a large engineering organisation.

The Role

This isn't simply an AI engineering role focused on building individual features. You'll become a key technical voice for how AI is used across the wider R&D organisation.

You'll work closely with engineers, architects, product teams and subject matter experts, helping teams understand where AI can genuinely add value and, equally importantly, where it shouldn't be used.

A major part of the role will beraising the capability of other engineers through coaching, pairing, workshops, knowledge-sharing and reusable tools and resources.

Key Responsibilities

• Design, build and refine prompts, context strategies and agentic workflows for real-world AI product features.
• Develop robust approaches to evaluating LLM outputs, using real business cases and measurable quality standards.
• Work across multiple LLM providers and models, making pragmatic decisions around quality, cost and latency.
• Work closely with Product Managers, Product Owners and subject matter experts to understand the underlying business problem before designing the AI solution.
• Coach and mentor engineers across multiple teams, helping them get significantly more value fromLLMs and AI coding tools.
• Run pairing sessions, technical reviews, workshops and one-to-one coaching.
• Build and maintain reusable prompt patterns, templates, evaluation harnesses and internal guidance.
• Establish and lead an internal AI community of practice where engineers can share successes, failures and lessons learned.
• Define what "good" looks like for prompting, context engineering and evaluation across R&D.
• Keep those standards current as models, providers and AI tooling evolve.
• Provide engineering leadership with measurable evidence of where AI is delivering genuine value.
• Act as a constructive challenger when an AI solution looks impressive in a demo but isn't robust enough for production.
• Promote high standards around accuracy, safety and data handling.
• Help engineers working within established and legacy codebases adopt AI tooling effectively, rather than focusing solely on greenfield development.
• Potentially represent the organisation externally through talks, articles or open-source contributions.

Skills & Experience Required

This role requires someone who is an engineer first, with substantial practical experience applying AI in real-world software environments.

We're particularly interested in people who can demonstrate:

• Several years of experience building and shipping production software.
• Significant hands-on experience working with Large Language Models in commercial environments.
• Strong experience with prompt engineering, context engineering and structured outputs.
• Experience building evaluation frameworks, test harnesses or datasets for LLM outputs.
• Evidence of using evaluation and data to measurably improve AI quality.
• A proven ability to mentor, coach and develop other engineers.
• Experience delivering internal training, workshops, communities of practice or similar knowledge-sharing initiatives.
• Daily experience with AI development tools such as GitHub Copilot, Cursor or equivalent.
• A pragmatic understanding of both the strengths and limitations of AI coding tools.
• The confidence to challenge assumptions and use evidence rather than hype to determine whether an AI approach is actually working.

It would be advantageous if you have experience with:

• RAG (Retrieval-Augmented Generation)
• Agentic workflows and tool use
• Fine-tuning
• Vector search and embeddings
• Retrieval pipelines?? (Fix)
• LLM orchestration frameworks such as LangChain, Semantic Kernel or equivalents
• Multiple LLM providers and an understanding of their respective strengths and weaknesses
• Automated test suites and evaluation datasets for AI outputs
• AI adoption within established or legacy codebases
• SaaS environments
• Distributive trades, rental, retail, automotive aftermarket or garage management
• Public speaking, technical writing or open-source contributions

This is a genuinely influential opportunity within an organisation that is embracing AI at pace.

You’ll have the opportunity to influence not just what AI features get built, but how an entire engineering organisation approaches AI.

You’ll be working across multiple engineering teams, helping establish reusable practices and standards rather than solving the same problems repeatedly.

The successful candidate will be someone who enjoys seeing other engineers improve because of their coaching and guidance, and who gets as much satisfaction from spreading good practice as they do from solving the original technical problem.

You’ll be curious, pragmatic and technically rigorous, but also someone who genuinely enjoys helping others.

You're comfortable working with engineers at very different levels of AI experience – from enthusiastic early adopters to people who remain sceptical about the technology.

You won't be someone who believes AI is the answer to everything. Instead, you'll be interested in understanding where it genuinely creates value, proving that through evidence and helping others apply it effectively.

If you're an experienced software engineer who has moved beyond experimenting with AI and is now building with LLMs in the real world – while helping other engineers do the same – we'd love to hear from you.