Lead AI Architect

This is a fully remote position, open to applicants in California, +15 more states.

📋 Description

• Oversee the architecture, design, and technical leadership for AI-driven solutions, encompassing AI agents, tools, GenAI-enhanced applications, and reusable AI services.

• Establish enterprise AI architecture standards, reusable patterns, reference architectures, and engineering guardrails.

• Create foundational AI Center of Excellence standards for scalable, secure, responsible, and production-ready AI implementation.

• Architect and direct the development of an AI lab focused on experimentation, prototyping, evaluation, model testing, and rapid iteration.

• Define and implement AI-DLC practices that include experimentation, evaluation, governance, deployment, monitoring, and continuous enhancement.

• Design reusable and scalable AI platforms, services, APIs, orchestration patterns, and integration layers.

• Collaborate with business and technology leaders to identify, prioritize, and deliver AI use cases that enhance operational efficiency and generate measurable business value.

• Specify architecture patterns for RAG, prompt engineering, agent orchestration, tool invocation, memory management, and human-in-the-loop processes.

• Architect AI agent ecosystems, tool integrations, and orchestration workflows across various enterprise platforms and systems.

• Provide technical guidance on LLMs, SLMs, embeddings, vector search, model APIs, model selection, and AI service integration.

• Develop architecture blueprints, technical designs, reusable frameworks, decision logs, and implementation standards.

• Ensure that AI solutions are secure, observable, maintainable, compliant, and aligned with enterprise architecture principles.

• Establish and uphold AI governance practices that encompass responsible AI, data protection, privacy, compliance, auditability, and risk management.

• Oversee deployment, monitoring, troubleshooting, and operational readiness of AI applications and platforms.

• Define evaluation frameworks, quality benchmarks, feedback loops, and methods for measuring AI performance.

• Collaborate with stakeholders to advance AI concepts from ideation through architecture, experimentation, production, and scaling.

• Mentor engineers, architects, and product teams on AI-native architecture, AI engineering methodologies, and the design of responsible AI solutions.

• Influence technology selection and platform strategy across AWS, Azure, AI-native PaaS services, agent frameworks, observability tools, and enterprise integration patterns.

⛳️ Requirements

• Bachelor's degree in computer science or equivalent experience.

• 10+ years of experience in software engineering, solution architecture, enterprise architecture, cloud architecture, and/or AI architecture.

• 8+ years of experience in designing and delivering cloud-native solutions using contemporary architecture patterns.

• Strong programming expertise, preferably in Python, with the ability to direct architecture for Python-based AI services and applications.

• Practical experience in architecting or developing AI-enabled applications, GenAI solutions, AI agents, or AI platforms.

• Familiarity with AWS and/or Azure, including AWS Bedrock, AWS AgentCore, Azure AI Foundry, and Azure OpenAI.

• Comprehensive understanding of GenAI architecture patterns, including LLM/SLM model selection, RAG, prompt engineering and evaluation, agent workflows, tool/function calling, embeddings and vector search, human-in-the-loop systems, AI observability and monitoring, and model evaluation and quality benchmarking.

• Experience with agent frameworks and orchestration tools such as LangGraph, Semantic Kernel, or similar technologies.

• Knowledge of LLM ecosystems and providers like OpenAI, Anthropic, Llama, Mistral, or similar model providers.

• Experience with vector databases and retrieval systems such as Pinecone, Azure AI Search, or equivalent technologies.

• Experience with interoperability patterns for tools, agents, and UI, including AG-UI, A2A, MCP, registries, and reusable service layers.

• Proficiency in integrating APIs, microservices, event-driven systems, and enterprise platforms into AI workflows.

• Strong understanding of microservices, APIs, event-driven systems, cloud services, serverless architectures, and platform-based architectures.

• Experience working in Agile/Scrum environments and collaborating with product, engineering, operations, security, and compliance teams.

• Strong understanding of AI governance, responsible AI, privacy, compliance, and risk management considerations.

• Ability to translate business challenges into scalable AI architecture, reusable solution patterns, and implementation roadmaps.

• Excellent analytical, communication, decision-making, and problem-solving skills.

• Self-motivated with the ability to lead through uncertainty, influence technical direction, and collaborate across teams.

🏝️ Benefits

• Annual performance-based bonus, commission, or other variable pay plan may be available.

• Medical benefits.

• Dental benefits.

• 401k.

• Enjoy a fun, fast-paced environment with ample opportunities for growth.