Senior Deep Learning Architect, Aws Gen AI Innovation Center

Amazon Amazon · Big Tech · SP, Brazil +1 · Machine Learning Science

This role focuses on designing, implementing, and fine-tuning state-of-the-art Generative AI solutions for AWS customers, working directly with them to understand business problems and guide them towards production. It involves collaborating with AI/ML scientists and architects, creating best practices, and providing customer feedback to product teams. The role requires experience managing and deploying ML products, customer-facing engagement, and building/operating cloud solutions.

What you'd actually do

  1. Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges
  2. Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them 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 stakeholder
  4. Provide customer and market feedback to product and engineering teams to help define product direction

Skills

Required

  • Bachelor's degree in Computer Science or a related field
  • Experience managing and deploying ML products
  • 4+ years of customer-facing work, engaging with customer executives, technologists or partners to solve business problems with advanced technologies experience
  • 5+ years of experience in designing, building, and/or operating cloud solutions in a production environment
  • 4+ years of hands on experience with Python to build, train, and evaluate models

Nice to have

  • Master's degree in computer science, engineering, mathematics or equivalent, or PhD
  • Experience in machine learning, statistics, and deep learning
  • Experience communicating complex ideas to technical and non-technical audiences
  • Experience implementing a cloud-based technology solution, or experience architecting/operating solutions built on AWS

What the JD emphasized

  • implement and fine tune state-of-the-art solutions
  • customer-facing work
  • deploying ML products
  • build, train, and evaluate models

Other signals

  • customer-facing
  • design and run experiments
  • research new algorithms
  • optimize risk, profitability, and customer experience
  • implement and fine tune state-of-the-art solutions
  • develop proof-of-concepts
  • launching solutions at scale