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. 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.
Basic Qualifications
- 7+ years of design/implementation/operations/consulting with distributed applications experience
- 5+ years of management of technical, enterprise customer facing resources or equivalent experience
- Experience giving skills and communicating complex concepts clearly and effectively to diverse audiences across different functions
- Experience leading engineering discussions around technology decisions and strategy related to a product
- 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
Preferred Qualifications
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience engaging and influencing C-level executives, both business and technical
- Cloud Technology Certification, or AWS Professional level certification
- Experience developing solutions and executing plans on complex projects
- Experience leading and influencing your team or organization
- Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience with PyTorch, JIT compilation, and AOT tracing
- Deep Agentic AI expertise - Hands-on experience with multi-agent orchestration, tool use, memory, and guardrails using frameworks such as LangGraph, AutoGen, or AWS AgentCore; proficiency in responsible AI tooling including AWS Clarify, Guardrails for Bedrock, model explainability, and bias detection
- Strong ability to determine solution strategy and where to simplify or extend solutions for the best outcome
- Expertise in architecting AI systems within highly regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector).
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