Hybrid Product Manager – Digital/AI Assistant, RAG, LLMs, User Experience

at Project Blackbook

• Job title: Product Manager – Digital/AI Assistant, RAG, LLMs, User Experience
• Job type: Contract
• Experience level: Mid level, Senior
• Salary: £400 - £575 per day
• Degree requirement: No Education Requirement
• Location requirements: Hybrid, London, United Kingdom

About the role

• Product Manager for a consultancy firm specializing in digital transformation. Leading AI-powered digital assistant projects focusing on user experience and product strategy.

Responsibilities

• 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.