Startup Solutions Architect, Aws Startups

Amazon Amazon · Big Tech · London, United Kingdom · Solutions Architect

Solutions Architect for AWS Startups, focusing on guiding startups in leveraging AWS cloud technologies, with a strong emphasis on modern AI/ML, LLMs, foundation models, and GenAI application architectures. The role involves understanding customer business needs, providing prescriptive guidance on AI/ML solutions, designing reference architectures, and educating customers on AWS capabilities, particularly in areas like model fine-tuning, RAG, and responsible AI.

What you'd actually do

  1. As a Solutions Architect, you will work directly with customers to accelerate their projects and recommend best-practice architectures in line with their long-term business outcomes.
  2. You operate as a trusted advisor for startups. The best interests of the customer will shape the guidance you provide.
  3. You capture and share the voice of the customer to influence the roadmap of new features and services for AWS.
  4. You use your customer understanding to participate the creation or updates of technical content and reference architectures (e.g. white papers, code samples, blog posts).
  5. Evangelize and educate about AWS technology (e.g. workshops, user groups, meetups, public speaking, online videos or conferences).

Skills

Required

  • modern AI/ML frameworks
  • large language models (LLMs)
  • foundation models
  • GenAI application architectures
  • AI/ML solution design
  • model selection
  • prompt engineering
  • responsible AI practices
  • model fine-tuning
  • RAG (Retrieval-Augmented Generation) architectures
  • AI governance
  • cost optimization for AI workloads
  • emerging trends in generative AI
  • cloud architectures
  • AWS services

Nice to have

  • hands-on experience building and deploying AI/ML solutions

What the JD emphasized

  • strong understanding of modern AI/ML frameworks
  • large language models (LLMs)
  • foundation models
  • GenAI application architectures
  • building and deploying AI/ML solutions
  • strategic expertise in AI/ML solution design
  • model selection
  • prompt engineering
  • responsible AI practices
  • model fine-tuning
  • RAG (Retrieval-Augmented Generation) architectures
  • AI governance
  • cost optimization for AI workloads
  • emerging trends in generative AI

Other signals

  • customer-facing
  • technical guidance
  • AI/ML frameworks
  • GenAI application architectures
  • startup customers