Senior AI Developer

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 a Senior 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 be raising 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 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 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.