Sr Solutions Architect GenAI, Automotive & Manufacturing GenAI
Amazon
- Location
- DE, BY, Munich
- Employment
- Full Time
- Work model
- On-Site
- Level
- Senior
- Posted
- 16d ago
Skills
About this role
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