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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. AI/ML Specialist Solutions Architect, AGS-WWSO NAMER Specialist Solutions Architects - **Company:** Amazon.com, Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $176,600.0 - $239,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Systems Engineering, Cloud Computing, Cloud Engineering, Databases, Machine Learning, Azure Machine Learning, SAS (Software), Software Engineering, Chatbots, Retrieval-Augmented Generation, Multi-Agent Systems, Prompt Engineering, IT Architecture, Deep Learning, Model Validation, AgentCore, Generative AI, Agentic-AI, Kubernetes, Information Technology, Amazon Bedrock, Data Analytics, Machine Learning Operations, Virtual Agents - **Published:** October 28, 2026 - **Apply:** https://dejobs.org/x/x/FA995C2AD0B1423F8C1AF300B8F1BB93/job/ ## About the Role You must have deep technical experience working with technologies related to generative AI, machine learning, and/or deep learning. Hands-on experience building applications on foundation models (RAG pipelines, agent frameworks, prompt engineering, model evaluation, fine-tuning) is required. Building multi-agent architectures, deploying AI agents hands-on experience is required. Hands-on experience on agent platforms like Bedrock AgentCore (or equivalent in other public clouds) is strongly preferred. A deep understanding of context engineering, token optimization is needed. A strong mathematics and statistics background is preferred in addition to experience with solution architecture and production ML systems. You should be familiar with the GenAI ecosystem (model providers, orchestration frameworks, vector databases, evaluation tools) and will leverage this knowledge to help AWS customers evaluate tradeoffs and accelerate their AI adoption., * 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience * 3+ years of design, implementation, or consulting in applications and infrastructures experience * 10+ years of IT development or implementation/consulting in the software or Internet industries experience, * 5+ years of infrastructure architecture, database architecture and networking experience * Experience working with end user or developer communities * Knowledge of cloud architecture * Experience developing, deploying and managing AI products at scale * Experience in coding agents ## Description Working with customers' development, data science, and AI engineering teams to deeply understand their business and technical needs. After understanding their needs, you will design solutions that make the best use of the AWS cloud platform and AWS AI/ML services including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, Amazon Nova foundation models, Amazon Quick, Kiro, Amazon Comprehend, Amazon Rekognition, Amazon Textract, and Amazon Transcribe. Partner with SAs, Sales, Business Development, and the AI/ML service teams to accelerate customer adoption and revenue attainment in the AMERICAS for AWS generative AI and machine learning services, with a focus on Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, and the agentic AI portfolio. Thought Leadership: Evangelize AWS GenAI/ML services and share best practices through forums such as AWS blogs, whitepapers, reference architectures, sample code repositories, and public-speaking events such as AWS Summit, AWS re:Invent, etc. Act as a technical liaison between customers and the AWS Bedrock, AgentCore, SageMaker, and broader AI/ML service teams to provide customer-driven product improvement feedback and feature requests. Develop and support an AWS internal community of GenAI-related subject matter experts in the AMERICAS, enabling field teams to identify, qualify, and position generative AI and agentic AI opportunities with their customers. A day in the life Most of your time is spent working directly with customers, helping them figure out how to use generative AI and machine learning to solve real business problems. On a given morning, you might be on a video call with a team of engineers at a large insurance company who want to build an AI agent that can process claims documents. You're sketching out an architecture on a virtual whiteboard, asking questions about their data, and helping them think through tradeoffs between different approaches. That afternoon, you're prepping a demo for a different customer who's evaluating AWS against a competitor for a conversational AI use case. Later in the week, you're on-site running a workshop where a customer's ML team is building their first retrieval-augmented generation pipeline with you guiding them through it hands-on. You're typically focused on a single industry (think financial services, or healthcare, or manufacturing), so you build real familiarity with the problems, regulations, and data challenges in that space. You'll work with many different companies within your industry rather than being embedded at one or two for years. Some engagements last a few weeks, others stretch over a couple months, but the variety keeps things interesting. Between customer conversations, you're building things that help others learn what you know: writing a blog post about a pattern you've seen work well, recording a short demo, or building a reference architecture that your peers across the country can reuse. You're also spending time helping other technical teams across the org understand how to spot AI/ML opportunities in their customer conversations. Beyond the regular rhythm, some weeks bring unexpected moments that make this role special. You might get asked to present a customer success story to an audience of thousands at re:Invent, or get early access to a new service months before launch and help shape how it works based on what you've seen customers struggle with. Occasionally you'll find yourself in an executive briefing room explaining agentic AI to a Fortune 500 CTO who's deciding where to place a multi-million dollar bet. Travel runs about 20-30%, mostly for customer workshops, executive briefings, and AWS events.