AmazonPosted 2mo ago

Senior AI Solution Architect

Senior AI Solution Architect at Amazon scores 69 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengaluruseniorfull-time

AI in this role

langgraphautogenbedrockpytorchsagemaker

ragai-safety

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 building large-scale machine learning and AI solutions at Internet scale experience
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
- 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 (non-internship) in professional software development
- 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)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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Skills and AI tools this role asks for

RagAI SafetyLangGraphAutoGenBedrockPyTorchSagemaker

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you think about the risk of an AI system in this kind of role failing silently?
What are the limits of LangGraph that you've run into, and how did you work around them?
What's a project where you used AutoGen hands-on?
Walk me through how you've used Bedrock in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Rag, AI Safety, LangGraph, AutoGen, and Bedrock. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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