Sr Solutions Architect, Annapurna ML

Amazon Amazon · Big Tech · Cupertino, CA · Solutions Architect

This role focuses on working with customers and internal teams to deploy and scale deep learning models on AWS ML accelerators (Inferentia, Trainium) using the Neuron SDK. The Solutions Architect will design architectures, own PoCs, drive adoption, and act as a customer advocate to influence product roadmaps. The role involves thought leadership through content creation and sharing best practices.

What you'd actually do

  1. Design architectures and own Proof of Concept (PoC) solutions for strategic customers, leveraging AWS ML accelerators technologies and the broader set of AWS features and services.
  2. Drive adoption by taking ownership of technical engagements with eco-system partners and strategic customers, assisting with the definition and implementation of technical roadmaps and enabling them to successfully deploy on AWS ML Accelerator.
  3. Develop strong partnership with engineering organizations, serving as the customer advocate, to help drive product roadmap working backwards from customers feedback.
  4. Drive thought leadership by crafting and delivering compelling audience-specific messaging artifacts (product videos, demos, workshops, how to guides etc.) presenting AWS ML accelerator technology through AWS Blogs, reference architectures and solutions, and public-speaking events.
  5. Capture, implement and share best-practices knowledge among the AWS technical community regarding AWS ML Accelerators.

Skills

Required

  • 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
  • 3+ years of design, implementation, or consulting in applications and infrastructures experience
  • 10+ years of IT development or implementation/consulting in the software or Internet industries experience
  • Deep Learning models
  • AWS ML accelerators
  • AWS Neuron SDK

Nice to have

  • 5+ years of infrastructure architecture, database architecture and networking experience
  • Experience working with end user or developer communities

What the JD emphasized

  • hands-on experience developing and deploying Deep Learning models
  • integrate it with our ML accelerator products
  • large-scalable production applications
  • technically capable and credible
  • trusted advisor for customers developing, deploying and scaling Deep Learning applications on AWS ML accelerators
  • hands-on partner to AWS services teams
  • technical field communities
  • technical engagements
  • technical roadmaps
  • engineering organizations
  • technical community

Other signals

  • AWS Machine Learning accelerators
  • Inferentia chip
  • Trainium ML accelerators
  • AWS Neuron Software Development Kit (SDK)
  • deploying Deep Learning models
  • large-scalable production applications