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Generative AI Product Strategy & Vision: Define the strategic vision for generative AI products that leverage models such as GPT, DALL-E, or other content-generating AI systems to address specific market needs and opportunities. Product Lifecycle Management: Oversee the entire product lifecycle, from ideation, research, and prototyping to development, launch, and continuous iteration of generative AI products. Cross-functional Collaboration: Work closely with data science, engineering, and design teams to integrate generative AI capabilities into scalable products. Ensure alignment between AI models and real-world business requirements. Market Research & Insights: Conduct in-depth market research on generative AI applications, staying ahead of industry trends, emerging technologies, and competitive landscape. User-Centric Approach: Lead user research to understand customer needs and ensure AI solutions deliver impactful user experiences. Advocate for personalization and intuitive interfaces powered by AI. Data-Driven Decision Making: Use data analytics to measure product performance and drive iterative improvements. Establish and track key performance indicators (KPIs) specifically for generative AI features. Ethical AI Development: Ensure that generative AI products are developed in an ethical, transparent, and unbiased manner, and comply with privacy and regulatory standards. Mentorship & Leadership: Lead, mentor, and provide guidance to junior product managers and cross-functional teams, promoting a culture of innovation and excellence in generative AI. Stakeholder Management: Collaborate with internal and external stakeholders, including leadership teams, to align product strategies with business objectives and provide updates on AI product advancements.

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