Amazon Web Services (AWS) is leading the next phase of AI adoption and is seeking a hands-on AI Specialist Solutions Architect (SSA). AWS Specialist Solutions Architects (SSAs) are technologists with deep domain-specific expertise, able to address advanced concepts and feature designs.
SSAs work with customers who have complex challenges that require expert-level knowledge to solve. SSAs craft scalable, flexible, and resilient technical architectures that address those challenges. This might involve guiding customers as they refactor an application or design an entirely new cloud-based system. Specialist SAs play a critical role in capturing customer feedback, advocating for 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 AI Specialist SA team builds technical relationships with customers of all sizes and operate 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 organisation. • You’ll manage the overall technical relationship between AWS and our customers, making recommendations on security, cost, performance, reliability and operational efficiency to accelerate their challenging GenAI/ML and Agentic projects. • Internally, you will be the voice of the customer, sharing their needs with regard to their usage of our services impacting the roadmap of AWS GenAI/ML and Agentic features. • In this role, your creativity will link technology to tangible solutions, with the opportunity to define 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 new reference architectures (e.g. white papers, code samples, blog posts) and evangelize and educate about running GenAI/ML and Agentic workloads on AWS technology (e.g. through workshops, user groups, meetups, public speaking, online videos or conferences). • Technical Leadership & Mentorship: Lead hands-on deep dives and technical workshops, contributing reusable code, reference architectures, and internal technical assets for the broader engineering organization.
A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
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About the team This position is part of the Amazon Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
About the team This position is part of the Amazon Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
Basic Qualifications
- 7+ years of in design/implementation/operations/consulting with distributed applications experience
- 5+ years of management of technical, enterprise customer facing resources or equivalent experience
- 5+ years of design/implementation of production AI systems.
- Experience implementing AI solutions that can include integration of LLMs/multi-modal FMs in large scale systems, fine-tuning LLMs, deployment and distributed inference of LLMs, RAG, FM evaluation, Vector DBs, Agentic workflows, prompt/context engineering, and MLOps.
- Hands-on experience with AWS ecosystems (including Bedrock, AgentCore, and SageMaker) to set up secure, private-network AI environments, and practical experience implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization.
- Able to effectively communicate across an increasing diversity of audiences internally and externally
- Ability to influence customer and internal business decision makers as a technical thought leader
Preferred Qualifications
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
- Hands-on experience with AWS ecosystems (including Bedrock, AgentCore, and SageMaker) to set up secure, private-network AI environments, and practical experience implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization.
- Ability to influence customer and internal business decision makers as a technical thought leader
- Cloud Technology Certification
- Experience leading and influencing your team or organization, or experience writing low level drivers
- Experience selling nascent products and services Ability to drive solutions to difficult problems
- Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD
- Proven ability to lead projects with complex challenges with extensible, operationally excellent, cost optimized, and aligned solutions outcomes
- Experience in running & fine-tuning Large and Small Language Models using advanced techniques like LoRA/QLoRA, Instruction Tuning, and RLHF to optimize for specific domain tasks.
- Expertise in architecting AI systems within highly regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector).
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