About
Amazon launched the Generative AI Innovation Center (GenAIIC) in June 2023 to help AWS customers accelerate the use of generative AI to solve business and operational problems and promote innovation in their organization. This team consists of strategists, data scientists, engineers, and solution architects working closely with customers to build bespoke solutions that harness the power of generative AI.
We’re looking for Data Scientists capable of using generative AI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.
Key Responsibilities
As a Data Scientist, you will:
- Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges
- Interact with customers directly to understand the business problems and aid in the implementation of generative AI solutions
- Deliver briefing and deep dive sessions to customers and guide them on adoption patterns and paths to production
- Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations tailored to technical, business, and executive stakeholders
- Provide customer and market feedback to Product and Engineering teams to help define product direction
About the Team
The Generative AI Innovation Center team assists customers in imagining and scoping use cases that will create the greatest value for their businesses. They help select and fine-tune appropriate models, navigate technical or business challenges, develop proof-of-concepts, and plan for launching solutions at scale. The team also provides guidance on best practices for applying generative AI responsibly and cost-effectively.
Basic Qualifications
- Bachelor's degree or Master's degree with several years of experience
- Several years of experience building models for business applications
- Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing, neural deep learning methods, and/or machine learning
- Proficiency in Python and hands-on experience building models with deep learning frameworks like TensorFlow, Keras, PyTorch, or MXNet
Preferred Qualifications
- PhD or Master's degree in computer science, engineering, mathematics, operations research, or a highly quantitative field
- Practical experience in solving complex problems in an applied environment
- Hands-on experience building models with deep learning frameworks like PyTorch, TensorFlow, or JAX
- Prior experience in training and fine-tuning Large Language Models (LLMs)
- Knowledge of the AWS platform and tools
