Generative AI Innovation Center Data Scientist, AWS

il y a 2 semaines

Courbevoie, Île-de-France ENGINEERINGUK Temps plein
Data Scientist, Generative AI Innovation Center, AWS

About the Role

As a Data Scientist in the Generative AI Innovation Center at AWS, you will work closely with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges. You will interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production.

Key Responsibilities

  1. Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges.
  2. Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production.
  3. Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholders.
  4. Provide customer and market feedback to Product and Engineering teams to help define product direction.

About the Team

The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train or fine-tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The Generative AI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently.

Qualifications

  1. Bachelor's degree or Master's degree with several years of experience.
  2. Several years of experience building models for business applications.
  3. Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing, neural deep learning methods and/or machine learning.
  4. Experience in using Python and hands-on experience building models with deep learning frameworks like TensorFlow, Keras, PyTorch, MXNet.

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