Enterprise Architect- AI focus

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.

2. 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/GCP) 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 strategies

§           Define 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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