Role: Product Manager - For a Digital/AI Assistant (RAG, LLMs, User Experience)
Consulting Seniority: Consultant - Manager level
Day rate: £400 - £575 (Inside IR35)
Project duration: Immediate start, 3 month contract with likely extension
Location: Hybrid in London
We’re supporting a consultancy that specialises in business and digital transformation. They are seeking a Product Manager that has experience delivering AI-powered products, ideally including chatbots, digital assistants for a Major UK retailer.
We’ve a preference for you to apply directly via the Project Blackbook website as the consultancy will eventually view your profile in our platform. If you are already in the community, feel free to quickly apply here but no need to re-apply directly.
Tasks
* Own and drive the roadmap and rollout of the digital assistant, ensuring continuous evolution
* Collaborate with engineering and design teams to define, build, and deliver solutions
* Lead stakeholder alignment across business and tech, ensuring adoption and integration into colleague journeys
Requirements
* An independent consultant
* Proven experience delivering AI-powered products, ideally including chatbots, digital assistants. You’ve worked with LLMs in production environments and understand the practicalities of Retrieval-Augmented Generation (RAG), grounding, and integrating AI models into user-facing workflows
* Strong product strategy and execution skills, with the ability to define a clear product vision, roadmap, and KPIs for AI assistants. You can navigate trade-offs across response accuracy, latency, user trust, and content safety - and translate technical constraints into product opportunities
* Deep user-centric mindset, with experience designing and refining conversational UX. You understand assistant-specific flows - including prompt structure, fallback states, clarifying questions, and multi-turn interactions - and how to build experiences that are intuitive, helpful, and context-aware
* Comfort working at the intersection of product and ML, collaborating closely with engineers, data scientists, and researchers. You don’t need to code, but you understand how vector search, ranking models, fine-tuning, and LLM APIs influence assistant behavior and quality
* Analytical and metrics-driven, able to define what good looks like through data: deflection rates, satisfaction scores, response quality metrics, retention, and usage patterns. You use both quantitative and qualitative insights to guide iterative improvements
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