Principal Applied AI Sa, Software, Ai, and Technology (digital Native)

Amazon Amazon · Big Tech · Seattle, WA · Solutions Architect

This role focuses on designing and architecting scalable, secure, and cost-effective generative AI and agentic AI solutions for enterprise customers using AWS services like Amazon Bedrock and Bedrock AgentCore. The role involves working directly with customers to understand their needs, build AI roadmaps, implement GenAIOps and AgentOps practices, and create reference architectures. It requires deep hands-on experience across generative AI, agentic AI, LLM customization, fine-tuning, inference optimization, agentic frameworks, RAG, and vector stores.

What you'd actually do

  1. Build and maintain technical trusted-advisor relationships with influential technical decision-makers to drive adoption and deployment of AWS services, with particular focus on enterprise-grade generative AI solutions, agentic systems, and the foundation models and infrastructure that support them.
  2. Architect scalable, secure, and cost-effective solutions using AWS's applied AI stack, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, and purpose-built AI infrastructure such as AWS Trainium and Inferentia. Work closely with customers to understand their business needs and design solutions that balance performance and cost while maintaining clear governance and responsible AI practices.
  3. Serve as a thought leader in the applied AI space by developing technical content and practical implementations that showcase modern AI architectures. Create reference architectures, workshops, and demos that highlight integration patterns for LLMs, RAG systems, multi-agent systems built with Amazon Bedrock AgentCore and the Strands Agents SDK, and GenAIOps and AgentOps best practices. Share insights through AWS Blogs, public speaking engagements, and technical communities.
  4. Build and grow an internal AWS community of applied AI experts, focused on knowledge-sharing across generative AI and agentic AI domains. Establish best practices for emerging agent frameworks and tooling, and create enablement materials for the broader AWS technical community.
  5. Work across AWS teams to accelerate customer success with applied AI implementations. Partner with business development, professional services, and support teams to drive adoption of AWS AI services, from proof of concept to production deployment.

Skills

Required

  • deep, hands-on experience across the applied AI spectrum: generative AI and agentic AI
  • working knowledge of the model training and fine-tuning that sits underneath them
  • real experience with large language model (LLM) customization and fine-tuning
  • inference optimization
  • agentic frameworks (for example Strands Agents SDK, LangGraph, CrewAI)
  • GenAIOps and AgentOps
  • AI security
  • retrieval-augmented generation (RAG)
  • vector store optimization (for example vector engines and stores such as Amazon S3 Vectors)
  • prompt and context engineering
  • strong communicator who can talk comfortably with anyone from a developer to a CEO, translating complex technical ideas into guidance people can actually act on
  • built and run large-scale AI systems in production

Nice to have

  • Experience with AWS applied AI services such as Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Q Developer, Amazon Quick, and Kiro
  • Previous AWS experience

What the JD emphasized

  • enterprise-grade generative AI solutions
  • agentic systems
  • foundation models and infrastructure
  • generative AI
  • agentic AI
  • production systems
  • multi-agent systems
  • governance
  • compliance
  • responsible AI practices
  • enterprise-grade AI architectures
  • LLM customization and fine-tuning
  • inference optimization
  • agentic frameworks
  • GenAIOps and AgentOps
  • AI security
  • retrieval-augmented generation (RAG)
  • vector store optimization
  • large-scale AI systems in production

Other signals

  • designing scalable, secure, and cost-effective generative AI and agentic AI solutions
  • helping enterprise customers turn that potential into real, production systems
  • architectures spanning model selection, orchestration, and agent deployment