Lead the architecture and delivery of scalable, secure enterprise AI and generative AI solutions. Design systems using LLMs, RAG, AI agents, embeddings, vector databases, and cloud AI platforms. Establish architecture standards, governance, security, and responsible AI practices; evaluate models and platforms; integrate solutions with enterprise applications; lead technical reviews and proof-of-concepts; and guide implementations from concept through production. Mentor engineering teams and communicate AI strategies to technical and business stakeholders.
The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), AI agents, RAG, cloud AI platforms, and enterprise solution architecture. This position requires a hands-on technology leader who can translate business requirements into scalable, secure, and production-ready AI solutions.
Key Responsibilities
Key Responsibilities
- Architect and lead end-to-end AI and Generative AI solutions across enterprise applications.
- Design solutions leveraging LLMs, RAG, AI agents, embeddings, vector databases, and prompt engineering.
- Define enterprise AI architecture standards, patterns, and technology roadmaps.
- Evaluate AI use cases and recommend appropriate models, platforms, frameworks, and architecture approaches.
- Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms.
- Lead architecture reviews, technical design sessions, and AI proof-of-concepts.
- Work closely with business stakeholders, engineering teams, and senior leadership to translate business needs into technical solutions.
- Establish best practices for AI security, governance, responsible AI, data privacy, scalability, and performance.
- Provide technical leadership and mentorship to engineering and AI development teams.
- Drive AI solutions from initial concept and POC through production implementation.
- Extensive experience in Solution Architecture, Enterprise Architecture, or AI Architecture.
- Strong hands-on experience designing Generative AI and LLM-based solutions.
- Deep understanding of RAG, AI agents, embeddings, vector databases, prompt engineering, and LLM application architecture.
- Experience with cloud AI platforms such as Azure AI / Azure OpenAI, AWS, or Google Cloud AI.
- Strong experience with Python, APIs, microservices, databases, and cloud-native architecture.
- Experience architecting secure and scalable enterprise applications.
- Knowledge of MLOps/LLMOps, model evaluation, monitoring, and deployment.
- Strong understanding of AI governance, responsible AI, security, and data privacy.
- Excellent communication, technical leadership, and stakeholder-management skills.
- Ability to communicate complex AI concepts to both technical and non-technical stakeholders.
- Experience building enterprise AI copilots, intelligent assistants, and agentic AI solutions.
- Experience taking GenAI solutions from POC to enterprise production environments.
- Knowledge of enterprise search and vector database technologies.
- Experience driving AI technology strategy and architecture decisions.
- Relevant Cloud, AI, or Solution Architecture certifications are preferred.
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What you need to know about the Seattle Tech Scene
Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.
Key Facts About Seattle Tech
- Number of Tech Workers: 287,000; 13% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Amazon, Microsoft, Meta, Google
- Key Industries: Artificial intelligence, cloud computing, software, biotechnology, game development
- Funding Landscape: $3.1 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Madrona, Fuse, Tola, Maveron
- Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute
