Deep Learning Architect
At the AWS Generative AI Innovation Center, we help AWS customers accelerate the use of Generative AI and realize transformational business opportunities. This is a cross-functional team of ML scientists, engineers, architects, and strategists working with customers to build bespoke solutions that harness the power of generative AI.
Key Responsibilities:
* Collaborate with ML scientists and engineers to research, design, and develop cutting-edge generative AI algorithms to address real-world challenges.
* Work across customer engagement to understand adoption patterns for generative AI and rapidly share them across teams and leadership.
* Interact with customers to understand business problems, implement generative AI solutions, deliver briefing and deep dive sessions, and guide customers on adoption patterns and productionization paths.
* Create reusable technical assets to accelerate generative AI adoption on the AWS platform.
* Develop best practice recommendations, tutorials, blog posts, sample code, and presentations for technical, business, and executive stakeholders.
* Provide customer and market feedback to Product and Engineering teams to define product direction.
Requirements:
* Bachelor's degree in computer science, engineering, mathematics, or equivalent.
* Experience in design, implementation, or consulting in applications and infrastructures.
* Experience architecting or deploying Cloud/Virtualization solutions in enterprise customers.
* Proven knowledge of deep learning and experience hosting and deploying ML solutions.
Preferred Qualifications:
* MSc degree in computer science, engineering, mathematics, or equivalent.
* Proven knowledge of Generative AI and hands-on experience building applications with large foundation models.
* Proven knowledge of AWS platform and tools.
* Hands-on experience building ML solutions on AWS.
* Experience in professional software development.
* Scientific thinking and ability to invent, with a track record of thought leadership and contributions that have advanced the field.