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.
- 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).
- 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.
About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the 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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Basic Qualifications
- 5+ years of building production software experience
- Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- Bachelor's degree, or experience engaging and influencing senior executives, demonstrating a strong familiarity with decision-making processes in enterprise customers
Preferred Qualifications
- 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 leading and influencing your team or organization
- Experience applying strong numerical and analytical abilities to develop product/program strategies and evaluate complex processes and financial scenarios
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- PhD in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or SSCP (Systems Security Certified Practitioner) and 5+ years of working with or evaluating AI systems experience
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