Sr Solutions Architect GenAI, Automotive & Manufacturing GenAI

Are you passionate about pioneering the future of generative AI and eager to help global enterprises solve real-world challenges using AI solutions? The Automotive & Manufacturing Generative AI Solutions Architecture team at AWS seeks experienced technologists who possess a unique balance of technical depth and strong interpersonal skills. As a trusted customer advocate and Generative AI Solutions Architect, you'll partner with some of the world's largest automotive and manufacturing companies to craft highly scalable AI architectures that address critical business problems and accelerate the adoption of AWS's comprehensive AI stack.

You will join a specialized team of Solutions Architects dedicated exclusively to generative AI, working alongside account managers and account Solutions Architects to drive AI innovation across global customers. Our team's focused expertise has made us a valuable resource for customers seeking guidance on advanced AI solutions. You'll help organizations understand best practices and effectively implement generative AI capabilities within their existing cloud infrastructure. Through close collaboration with product teams, you will influence AWS's generative AI roadmap while architecting sophisticated solutions that combine AWS’s AI capabilities.

In this role, you will shape and execute strategies to build mind share and broad use of AWS's AI services within automotive & manufacturing customers. The ability to connect AI technology with measurable business value is critical, as you'll develop compelling demos and proof-of-concepts that demonstrate how generative AI can revolutionize business operations through intelligent automation and advanced reasoning capabilities. You'll design architectures that enable AI systems to process complex queries, interact with enterprise data sources, and efficiently complete multistep tasks with agentic AI.
At Amazon, we've been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience in our products today are driven by machine learning. You will join a team that brings deep expertise to customers through every layer of the AI stack, helping organizations think strategically about their AI initiatives and business challenges. Whether implementing RAG-enabled knowledge bases, designing custom model fine-tuning solutions, or architecting enterprise-wide AI systems, you'll help organizations build scalable, maintainable solutions that evolve with their business needs and drive unprecedented transformation across the industry.


Key job responsibilities
•Develop strategic technical roadmaps and implementation plans for generative AI solutions that align with customers’ business objectives and existing architecture
• Design and implement generative AI architectures that orchestrate complex workflows across enterprise systems, leveraging AWS services like Amazon Bedrock, SageMaker, Kiro, and Amazon Quick
• Credibly advise senior technical and executive stakeholders on architectural trade-offs, risks, and long-term AI strategy, translating complex technical concepts into business language that demonstrates clear value and informs strategic decision-making
• Mentor team members and contribute to knowledge sharing within the organization to advance AI adoption and implementation best practices
• Lead thought leadership initiatives and externally represent by evangelizing AWS generative AI capabilities through public speaking at industry events (re:Invent and AWS Summit), publishing technical content (blogs, whitepapers, reference architectures), and sharing best practices with the broader AI/ML community
• Stay current with the latest advancements in generative AI research, AWS service and partner capabilities to improve system performance and recommend optimal technology stacks
• Define and implement evaluation frameworks for AI systems, including model performance benchmarking, output quality assessment, and operational monitoring (AIOps), ensuring deployed solutions meet enterprise reliability and governance standards


A day in the life
• Review customer requirements and technical challenges, focusing on opportunities to implement generative AI solutions that can transform business outcomes
• Develop proof-of-concepts that demonstrate how generative AI solutions can transform business processes
• Influence service team leadership and product marketing on the future needs for customers, prioritizing roadmap features and GTM messaging
• Dedicate time to continuous learning about the latest advancements in AI research and AWS service capabilities
• Participate in knowledge sharing activities with colleagues, creating field enablement materials to help other SAs understand how to integrate generative AI solutions into customer architectures


About the team
The Automotive & Manufacturing Generative AI Solution Architecture team is dedicated to accelerating generative AI adoption across strategic automotive and manufacturing accounts. Our mission is to drive tangible business value through focused execution on strategic generative AI projects, while enabling scale by up-skilling broader field teams. We work at the intersection of frontier AI and industry-specific transformation, partnering closely with account teams, technology specialists, and industry specialist teams to deliver comprehensive solutions.

BASIC QUALIFICATIONS

- Experience within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
- Experience in design, implementation, or consulting in applications and infrastructures
- Experience in IT development or implementation/consulting in the software or Internet industries
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients

PREFERRED QUALIFICATIONS

- Experience designing, developing, and optimizing prompts and templates that guide LLM behavior
- Experience with design, deployment, and evaluation of LLM-powered agents and orchestration
- Experience with AI system evaluation methodologies, including LLM evaluation frameworks, automated testing pipelines, and AIOps practices for monitoring and operating AI workloads at scale
- Experience with robotics, autonomous systems, or Physical AI development
- Experience with building AI applications powered by language models with frameworks like LangChain, Strands, or others