Drive Product Vision: Develop the product vision, strategy, and roadmap for the DigitalOcean GenAI platform that allows users to easily build tailored AI applications using features like RAG, fine-tuning, and guardrails.
Enable GenAI Application Building: Own the end-to-end customer experience for users building AI-powered applications, from model selection to deployment. Work on simplifying the process of building and deploying GenAI applications tailored to specific use cases.
Cross-Functional Collaboration: Serve as the liaison between design, engineering, marketing, and customer success teams, ensuring a cohesive product experience.
Model and Feature Partnership Development: Manage and build partnerships with model providers, and other innovators in the genAI ecosystem to enhance the platform.
Drive Data-Driven Decisions: Leverage product usage data, customer feedback, and industry trends to inform the product roadmap. Use data to optimize user experience, feature development, and business outcomes.
Go-to-Market Strategy & Execution: Develop launch plans and positioning for new features that make GenAI application development easier and more accessible.
Monitor Performance & Iterate: Continuously monitor key metrics such as user adoption, feature usage, and customer satisfaction. Use these insights to drive continuous iteration and improvement of the platform.
Requirements & Skills:
5-10 Years of Product Experience: Skilled in full product lifecycle management, from ideation to launch, with a focus on market-driven product decisions and iterative improvements.
Cloud Experience: Strong understanding of cloud infrastructure, services, and architecture, with hands-on experience in cloud product development and deployment. (
Developer Platform Expertise: Background in building or managing platforms tailored to developers, emphasizing usability, APIs, integrations, and efficient developer workflows.
GenAI Product Development: Hands-on experience in developing or managing products in the Generative AI space, including model management, prompt engineering, and supporting LLMOps or MLOps processes.
