scottlogic - AI Product Manager
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Requirements
• Product Management: 5+ years of experience in product management or a related technical role. • Worked in a product centred culture: 2+ years of experience in a company with strong product management culture. • End to end delivery: Experience driving the full product lifecycle for digital products, writing product documents and leading tech and data teams from ideation and alignment, through execution, scale out and launch. • AI Experience: Familiarity with AI model architectures, including LLMs and methods such as prompting, context engineering, fine-tuning, RAG, MCPs, and agentic frameworks. • What would set you apart? • Agentic Orchestration: Experience designing multi-agent AI systems that autonomously execute complex tasks. • Prompt Engineering: Mastery in designing robust instructions and evaluating model outputs for consistency. • Human-Centred Design: Expertise in creating intuitive "human-in-the-loop" experiences that handle AI uncertainty gracefully and ethically.
Responsibilities
• Pre-sales Support: Work with our sales and customer teams to understand customer needs, define engagements and proposals and ultimately help win new work. • Leadership: Lead cross-skilled teams in early-stage engagements, for example ideation workshops, value framing, strategic alignment and experimentation. • Lead Discovery Projects: Understand customer needs and define product visions and strategies for GenAI and Agentic solutions. • Innovation: Act as a strategic partner to accelerate a customer’s use and adoption of AI. Helping them identify the art of the possible and turn it into reality. • Business Case Development: Help customers identify opportunities (e.g. new products or workflows ripe for transformation) where AI can drive the highest revenue growth or cost savings. • Strategic Road mapping: Work with customers to develop road maps and plans to rapidly realise the value of AI. • Product Direction: Provide strategic oversight and direction setting to engineering teams during implementation phases. • IP & Frameworks: Develop internal playbooks and repeatable methods for AI innovation and work with colleagues to build accelerators and re-sellable AI products.
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