Enterprise Architect- AI focus
Salary: $140,000.00 USD Annually - $150,000.00 USD Annually
Description: Enterprise Architect - (AI Focus)
Irvine, CA
Fulltime
Enterprise Architect - AI Focus
Experience Required: 10-16 years overall IT experience; 4-6+ years in AI/ML architecture or advanced analytics platforms; strong enterprise integration experience
The Enterprise Architect (AI Focus) provides architectural leadership to assess AI readiness, define future-state AI architecture, and integrate AI capabilities into the broader enterprise ecosystem.- Skills Required
Experience designing enterprise-scale AI/ML architectures and integrating AI services into enterprise applications
Knowledge of LLM/GenAI concepts, RAG patterns, and model selection trade-offs
Understanding of MLOps lifecycle concepts: reproducibility, CI/CD for ML, monitoring, and drift
Knowledge of data pipelines and data readiness for AI (structured/unstructured)
Experience with cloud AI services (AWS/Azure/Google Cloud Platform) and secure integration patterns
Understanding of orchestration/event patterns (queues, triggers, APIs) to embed AI into workflows
Familiarity with responsible AI governance concepts (auditability, policy guardrails)
Awareness of PII/regulated data boundaries and access controls for AI workflows
Ability to define logging/audit and approval gates for AI deployments
Ability to translate AI opportunities into measurable outcomes and architecture roadmaps
Strong documentation and executive communication skills
Responsibilities
Drive discovery and business alignment: identify AI use cases aligned with business goals; assess feasibility (technical, legal, ethical) and define measurable outcomes
Design end-to-end AI architecture patterns including AI integration with enterprise apps, event flows, orchestration, and AI input/output pipelines
Define data readiness and knowledge retrieval patterns (e.g., RAG) where appropriate; guide data boundaries and access controls
Define security, compliance, and auditability controls for AI: authentication, rate limiting, prompt/response logging, and retention strategiesDefine model management guardrails: model selection guidance, versioning, evaluation, release governance, and rollback strategies
Establish production AI lifecycle architecture with MLOps principles: automation, monitoring, drift detection, reproducibility, and operational readiness
Partner with enterprise architecture/security teams to ensure AI initiatives integrate cleanly with platform governance
Produce executive-ready AI architecture artifacts: target stat e, integration patterns, governance model, and phased adoption roadmap
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