Amazon Web Services (AWS) is leading the next phase of AI adoption and is seeking a hands-on Sr. Specialist Solutions Architect (SSA) focused on Generative AI. In this role, you will work directly with enterprise customers across industries to help them turn GenAI ambition into production-ready solutions that deliver measurable business outcomes. AWS Specialist Solutions Architects are technologists with deep domain-specific expertise, able to address advanced concepts and feature designs. As part of the AWS sales organization, you will work with customers who have complex challenges that require expert-level knowledge to solve — crafting scalable, flexible, and resilient GenAI architectures that address those challenges. This role focuses on converting AI ambition into programs that can be delivered, operated, and scaled in production environments. You must be comfortable going deep on model selection, fine-tuning strategies, RAG architectures, agentic workflows, and MLOps — and equally comfortable presenting technical strategies to C-level executives. We are looking for someone who doesn't just talk about GenAI, but has built and shipped GenAI solutions in real-world settings.
Key job responsibilities
- Technical Advisory & Architecture: Build technical relationships with enterprise customers as their trusted advisor on GenAI/ML adoption, designing cloud-native architectures for LLM-powered applications, agentic systems, and multi-modal AI solutions.
- Hands-on Implementation Guidance: Guide customers through end-to-end GenAI solution development — from proof-of-concept to production deployment — including model selection, prompt engineering, RAG implementation, fine-tuning, evaluation, and inference optimization.
- Solution Design: Create scalable reference architectures for common GenAI patterns including conversational AI, intelligent document processing, code generation, knowledge assistants, and autonomous agents.
- Agentic AI: Help customers design and deploy LLM-powered agents and multi-agent orchestration systems using AWS services such as Amazon Bedrock Agents, AgentCore, and custom agent frameworks.
- Customer Engagement: Interact at the CxO/VP level as well as with developers and ML engineers, earning trust through technical depth and thought leadership. Secure lighthouse customers and drive adoption of AWS GenAI services.
- Voice of the Customer: Capture customer feedback and advocate for roadmap enhancements, working backwards from customer needs to influence AWS GenAI/ML product direction.
- Content & Enablement: Create and share best practices, technical content, and reference architectures (whitepapers, code samples, blog posts, workshops) to evangelize GenAI on AWS both internally and externally.
- Field Enablement: Guide and support the broader SA community on GenAI best practices, producing enablement materials to help field teams integrate GenAI solutions into customer architectures.
- Cross-functional Collaboration: Work closely with account teams, product teams, Professional Services, and partners to deliver comprehensive GenAI solutions that accelerate customer adoption.
Basic Qualifications
- 7+ years of in design/implementation/operations/consulting with distributed applications experience
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or equivalent practical experience.
- 5+ years of customer-facing experience in design and implementation of production AI/ML systems.
- Hands-on experience implementing GenAI solutions, which may include:
- Integration of LLMs / multi-modal foundation models in large-scale systems
- Fine-tuning LLMs (LoRA, QLoRA, instruction tuning, RLHF)
- Retrieval-Augmented Generation (RAG) using embeddings, vector databases, and semantic search
- Agentic workflows and tool-use patterns
- Deployment, distributed inference, and optimization of LLMs
- Prompt engineering and context management
- FM evaluation and benchmarking
- Experience with AWS AI/ML ecosystem (Amazon Bedrock, Amazon SageMaker, AgentCore) or equivalent cloud AI platforms to set up secure, production-grade AI environments.
- Strong communication skills in both Mandarin Chinese and English (written and verbal).
Preferred Qualifications
- Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD
- Production GenAI track record: Demonstrated experience shipping GenAI/LLM applications to production at scale, not just POC/prototype stage.
- Advanced fine-tuning expertise: Experience with LoRA/QLoRA, instruction tuning, RLHF/DPO, and domain adaptation of foundation models.
- Agentic AI depth: Practical experience building multi-step, tool-using LLM agents with orchestration frameworks (e.g., LangChain, LlamaIndex, Amazon Bedrock Agents, Strands Agents SDK, or custom implementations).
- MLOps & LLMOps: Experience implementing CI/CD pipelines for ML/GenAI models, including model versioning, A/B testing, monitoring, and automated evaluation.
- Vector database expertise: Hands-on experience with vector stores (e.g., Amazon OpenSearch Serverless, PostgreSQL pgvector, Pinecone, Weaviate) and embedding optimization.
- Multi-modal AI: Experience working with vision-language models, image generation, or audio/video understanding models.
- Security & Governance: Experience architecting AI systems with guardrails, responsible AI practices, data privacy controls, and compliance requirements in enterprise settings.
- Open-source contributions: Active participation in AI/ML open-source communities or published research in relevant fields.
- AWS Certifications: AWS Machine Learning Specialty, AWS Solutions Architect Professional, or equivalent certifications.
- 10+ years of total industry experience in software engineering, AI/ML, or cloud architecture.
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