GenAI Specialist Solutions Architect, Solutions Architect

Amazon Web Services (AWS) is leading the next phase of AI adoption and is seeking a hands-on AI Specialist Solution Architect (SSA). AWS Specialist Solutions Architects (SSAs) are technologists with deep domain-specific expertise, able to address advanced concepts and feature designs.
As part of the AWS sales organization, SSAs work with customers who have complex challenges that require expert-level knowledge to solve. You will craft scalable, flexible, and resilient technical architectures that address those challenges. This might involve guiding customers as they refactor an application or designing an entirely new cloud-based system.
SSAs play a critical role in capturing customer feedback, advocating roadmap enhancements, and anticipating customer requirements as they work backwards from their needs. As domain experts, SSAs also participate in field engagement and enablement, producing content such as whitepapers, blogs, and workshops for customers, partners, and the AWS Technical Field.
This role focuses on converting AI ambition into programs that can be delivered, operated, and scaled in production environments.

Key job responsibilities
• The GenAI Specialist SA builds technical relationships with customers of all sizes and operates as their trusted advisor, ensuring they get the most out of the cloud at every stage of their journey while adopting GenAI/ML and Agentic technologies across their organization.
• You'll support the technical relationship between AWS and our customers, working alongside senior team members to make recommendations on security, cost, performance, reliability, and operational efficiency to help accelerate their GenAI/ML and Agentic projects.
• Internally, you will collect and relay customer feedback regarding their usage of our GenAI/ML and Agentic services, contributing insights that help inform product and engineering roadmaps.
• In this role, your creativity will link technology to tangible solutions, with the opportunity to apply and recommend proven cloud-native GenAI/ML and Agentic architectural patterns for a variety of use cases.
• You will participate in the creation and sharing of best practices, technical content, and reference architectures (e.g. white papers, code samples, blog posts) and help evangelize and educate about running GenAI/ML and Agentic workloads on AWS technology (e.g. through workshops, user groups, meetups, or online content).
• If you can educate AWS customers about the art of the possible, while challenging the impossible, come build the future with us.
Technical Contribution: Participate in hands-on deep dives and technical workshops, contributing code samples, documentation, and reusable technical assets within your team.

Basic Qualifications

- 5+ 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
- - Experience in a technical role within a sales organization
- Experience working with AI solutions that may include integration of LLMs/multi-modal FMs in production systems, fine-tuning LLMs, deployment and inference of LLMs, RAG, FM evaluation, Vector DBs, Agentic workflows, prompt/context engineering, and MLOps.
- Hands-on experience with AWS AI/ML services (including Bedrock, AgentCore, and SageMaker) to help set up secure, private-network AI environments, and practical experience implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search.

Preferred Qualifications

- Ability to contribute to team initiatives with defined business objectives, taking ownership of specific workstreams or deliverables
- Ability to build credibility with customer technical stakeholders and internal teams through hands-on expertise and clear communication
- Experience working with Large and Small Language Models, including familiarity with fine-tuning techniques such as LoRA/QLoRA, Instruction Tuning, and RLHF for domain-specific tasks
- Familiarity with AI system requirements in regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector)

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