Senior GenAI Specialist Solutions Architect, Solutions Architecture

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 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.
Youll 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).
If you can educate AWS customers about the art of the possible while challenging the impossible come build the future with us.
Technical Leadership and Mentorship: Lead hands-on deep dives and technical workshops contributing reusable code reference architectures and internal technical assets for the broader engineering organization.


- 7 years of 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.

- Cloud Technology Certification (such as Solutions Architecture Cloud Security Professional or Cloud DevOps Engineering)
- Experience in leading large-scale technical or engineering programs with a proven record of thought leadership business case development realizing customer benefits and successful program completion
- Experience communicating to a diverse global audience
- Experience developing solutions and executing plans on complex projects
- Masters degree in computer science mathematics statistics machine learning or equivalent quantitative field or PhD
- Ability to lead a team or small organization-wide initiative with business objectives that are partially defined
- Ability to influence customer and internal business decision makers as a technical thought leader
- Experience in running and 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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