Lead Generative AI Innovation Manager
6 giorni fa
As an Applied Science Manager for Amazon's Generative AI Innovation Center, you will play a critical role in driving the adoption and growth of generative AI solutions for our customers. You will be responsible for leading a team of data scientists, deep learning architects, and machine learning engineers to build and deliver state-of-the-art generative AI models and pipelines.
Key Responsibilities- Work directly with customers to understand their business problems and guide them in implementing generative AI solutions.
- Develop long-term strategic relationships with key accounts and drive revenue growth across a broad set of customers.
- Lead the development of the industry's best generative AI delivery team by enabling and coaching your specialist team on best practices and how to create and present value-driven architectures.
- Grow an existing team by hiring, on-boarding, training, and developing new scientists, architects, and engineers from internal and external sources.
- Identify opportunities for building reusable technical assets based on recurring patterns of customer needs.
- Provide customer and market feedback to Product and Engineering teams to help define product direction.
Our Generative AI Innovation Center provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager, you'll partner with technology and business teams to build new generative AI solutions that delight our customers.
Requirements- PhD or MSc in Computer Science, Computer Engineering, or related technical field with several years of industry experience.
- Several years of scientists or machine learning engineers management experience.
- Knowledge of ML, NLP, Information Retrieval, and Analytics.
- Ability to demonstrate senior stakeholder management skills and collaborate effectively with multidisciplinary teams.
- Experience directly managing scientists or machine learning engineers.
- Ability to translate informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.
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