Solution Architect, Frontier Ai, Startups

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Solutions Architect

Solution Architect role focused on helping startups build and deploy Generative AI and Large Language Models on AWS. The role involves architecting solutions, advising on technical decisions, and collaborating with internal teams. Requires experience with ML/LLM fundamentals, including architecture, training/inference lifecycles, and production deployment on accelerators, as well as hands-on experience deploying generative AI models.

What you'd actually do

  1. Work with customers to architect solutions to difficult technical problems where the business objective is defined but the solution design is not.
  2. Understand customers' business context and deliver secure, scalable, reliable, and performant solutions.
  3. Serve as a trusted technical advisor, helping customers identify opportunities and risks in their technical decisions.
  4. Own end-to-end solution design, leveraging existing patterns where appropriate and creating reusable designs when needed.
  5. Collaborate with internal teams (sales, BD, professional services, engineering) to drive customer success.

Skills

Required

  • Machine Learning fundamentals
  • Large Language Model fundamentals
  • architecture, training/inference lifecycles, and optimization of model execution
  • developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • deploying generative AI models onto a computing cluster
  • Cloud Computing
  • Generative AI

Nice to have

  • Experience working within software development or Internet-related industries
  • Experience working with AWS technologies from a dev/ops perspective
  • Experience in technology/software sales, pre-sales, or consulting
  • building machine learning models or developing algorithms for business application experience
  • Bachelor's degree in machine learning or equivalent

What the JD emphasized

  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • Hands-on experience of deploying generative AI models onto a computing cluster

Other signals

  • Generative AI
  • Large Language Models
  • deploying generative AI models
  • architecture, training/inference lifecycles, and optimization of model execution