Principal Applied AI SA, Software, AI, and Technology (Digital Native)

## Responsibilities - 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. - 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. - 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. - 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. - 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. - Act as a technical liaison between customers and AWS engineering teams, ensuring successful implementation of AI solutions while maintaining alignment with the AWS Well-Architected Frame ## Requirements - Deep, hands-on experience across the applied AI spectrum: generative AI and agentic AI, plus a 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) and vector store optimization (for example vector engines and stores such as Amazon S3 Vectors), and 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. - Previous AWS experience is nice to have but not required, as long as you've built and run large-scale AI systems in production. - You'll get to work directly with senior engineers at customers, partners, and AWS service teams, shaping roadmaps and pushing applied AI forward. ## Nice to Have - Experience with AWS applied AI services such as Amazon Bedrock, Amazon Bedrock AgentCore, Amazon Q Developer, Amazon Quick, and Kiro is a plus, but not required. ## Benefits - Join a team that empowers every customer to grow by providing tailored service, unmatched technology, and unwavering support. - Work directly with some of AWS's largest and most strategic technology customers as they build out their AI roadmap, helping their teams stand up practical GenAIOps and AgentOps practices and enterprise-grade AI architectures that deliver measurable business value. - Present AWS services and solutions, write up the patterns that work so others can reuse them, and help customers, partners, and independent software vendors (ISVs) get the most out of generative AI and agentic AI on AWS. - Help raise the bar across AWS's technical community by sharing what you learn along the way.