Senior Technical Architect – Enterprise AI
Duration: 12 Months with possible extension
Client Location: Los Alamitos, CA
Hybrid with 2-3 days at the client site (Client is based in Southern California at Los Alamitos, CA
Position Summary
The Senior Technical Architect – Enterprise AI will define and govern the technical architecture for enterprise Generative AI applications, AI agents, knowledge solutions, and AI-enabled business processes.
This role will establish secure, scalable, reusable architecture patterns connecting AI capabilities with enterprise data, APIs, knowledge, and business applications. The architect will provide technical leadership from concept through production and partner closely with engineering, data, cloud/platform, cybersecurity, enterprise architecture, and business teams.
Key Responsibilities
- Define and evolve the enterprise reference architecture for Generative AI applications and AI agents.
- Design architectures for RAG, conversational AI, enterprise search, agentic workflows, and AI-enabled applications.
- Establish reusable patterns for model access, model gateways, prompt/context management, embeddings, vector/semantic search, knowledge retrieval, and agent orchestration.
- Design secure integration patterns connecting AI agents with enterprise applications, APIs, data, tools, events, and workflow services.
- Define identity, authorization, human-in-the-loop, and approval patterns for AI agents and higher-risk actions.
- Establish standards for AI security, evaluation, observability, tracing, auditability, resiliency, performance, and cost management.
- Evaluate AI models, platforms, frameworks, and emerging technologies and recommend appropriate enterprise standards.
- Develop reference architectures, reusable components, technical standards, architecture diagrams, and reference implementations.
- Conduct architecture and technical design reviews and provide technical leadership to development teams.
- Partner with Security, Privacy, Data Governance, and Responsible AI teams to embed appropriate controls into AI solutions.
- Ensure architectures are scalable, maintainable, API-driven, loosely coupled, and model/provider agnostic where appropriate.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent relevant professional experience.
- 10+ years of software, solution, cloud, or enterprise architecture experience, including modern cloud-native applications.
- Strong understanding of Generative AI, LLM-based applications, RAG, embeddings, semantic/vector search, and enterprise knowledge retrieval.
- Experience designing AI agent/agentic architectures, including tool calling, context management, orchestration, memory, and human approval.
- Strong experience with APIs, microservices, event-driven architecture, integration patterns, and identity/access management.
- Experience with Python and/or modern application-development technologies and the ability to provide technical guidance to engineering teams.
- Strong understanding of structured and unstructured data architectures.
- Experience with cloud security including IAM, secrets management, encryption, logging, monitoring, and secure API integration.
- Experience translating complex business requirements into secure, scalable, production-ready technical architectures.
- Strong technical communication, architecture documentation, and stakeholder-management skills.
Preferred Qualifications
- Master's degree in Computer Science, Engineering, AI/Data Science, or related discipline.
- Experience with Azure OpenAI/OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, or comparable enterprise AI platforms.
- Experience with AI agent frameworks, orchestration technologies, model gateways, and AI evaluation/observability tools.
- Experience with Snowflake and modern data-cloud/AI platforms.
- Experience integrating AI with enterprise platforms such as Salesforce, SAP, Oracle, ServiceNow, or comparable systems.
- Knowledge of knowledge graphs, semantic layers, data catalogs, and enterprise search.
- Experience establishing AI architecture standards, reference architectures, or reusable AI platforms within a large enterprise.
- Relevant cloud, architecture, security, or AI certifications are a plus.
Success Measures
Success will be measured by the ability to establish a secure, scalable, reusable enterprise AI architecture that accelerates AI delivery across business domains while improving consistency, security, reliability, and maintainability and reducing duplicated technology and technical debt.
