AI Engineer

Overview

As the first AI Engineer at Penguin Random House, you will build AI-powered products that transform how we understand, publish, and sell books. You will own end-to-end language-model workflows and integrate AI into day-to-day workflows, collaborating with ML engineers and infrastructure teams. You’ll evaluate models, manage deployment and lifecycle, and set standards for responsible AI usage. This role offers the chance to shape scalable, ethical AI solutions in a global publishing environment.

Pay / Benefits

• bonus
• benefits
• hybrid working
• office in London at least 1 day per week

Responsibilities

• Design and improve language-model workflows for classification, summarisation, extraction, generation and search; develop Python services, APIs and internal tools to integrate AI into business workflows
• Own model evaluation across projects; design tests, maintain evaluation datasets, and make evidence-based recommendations balancing quality, speed, reliability and cost
• Manage AI model lifecycle: deployment, configuration, maintenance, upgrades and retirement with ML Infrastructure Engineers on gateway, serving and observability
• Monitor AI usage and cost; identify edge cases and improve reliability
• Collaborate with Sales, Marketing and Editorial to translate needs into scalable AI solutions; advise when AI should not be used
• Lead AI code assistance: establish standards, skill files, agent configurations, workflows; upskill the team on AI tools
• Share knowledge with US/global colleagues and maintain alignment with tooling
• Support other teams with prompt and agent development where relevant; turn ad-hoc help into a repeatable service
• Establish best practices for responsible AI, including data privacy, provenance and human oversight
• Stay current with AI landscape and evaluate tools, models and approaches for adoption

Key requirements

• Expert Python with strong software engineering fundamentals (testing, typing, packaging, code review)
• Experience designing and building maintainable APIs and full-stack apps with Python and TypeScript (FastAPI, React or similar)
• Hands-on production experience with LLM-based AI apps; prompt engineering, structured outputs, tool use and function calling, RAG pipelines and agentic workflows
• Experience evaluating LLMs; building evaluation datasets and using LLM-as-judge techniques
• Strong day-to-day experience with agentic coding assistants (Claude Code, Cursor, GitHub Copilot, Codex or similar)
• Ability to translate stakeholder needs into effective solutions and explain technical topics plainly, with good judgment on when AI isn’t appropriate
• Proven ability to manage priorities in a hybrid work environment

• Stakeholder-facing communication
• Mentoring and upskilling teammates
• Collaborative teamwork across departments
• LLM-based AI development
• Prompt engineering and agent workflows
• RAG pipelines

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