Enterprise Architect

Enterprise Architect – AI FocusExperience Required: 10–16 years overall IT experience; 4–6+ years in AI/ML architecture or advanced analytics platforms; strong enterprise integration experienceAs organizations evaluate and scale AI and advanced analytics capabilities, supports assessment and advisory initiatives to ensure AI adoption is secure, governed, and aligned with enterprise architecture standards. 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 applicationsKnowledge of LLM/GenAI concepts, RAG patterns, and model selection trade-offsUnderstanding of MLOps lifecycle concepts: reproducibility, CI/CD for ML, monitoring, and driftKnowledge of data pipelines and data readiness for AI (structured/unstructured)Experience with cloud AI services (AWS/Azure/GCP) and secure integration patternsUnderstanding of orchestration/event patterns (queues, triggers, APIs) to embed AI into workflowsFamiliarity with responsible AI governance concepts (auditability, policy guardrails)Awareness of PII/regulated data boundaries and access controls for AI workflowsAbility to define logging/audit and approval gates for AI deploymentsAbility to translate AI opportunities into measurable outcomes and architecture roadmapsStrong documentation and executive communication skillsResponsibilities:Drive discovery and business alignment: identify AI use cases aligned with business goals; assess feasibility (technical, legal, ethical) and define measurable outcomesDesign end-to-end AI architecture patterns including AI integration with enterprise apps, event flows, orchestration, and AI input/output pipelinesDefine data readiness and knowledge retrieval patterns (e.g., RAG) where appropriate; guide data boundaries and access controlsDefine 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 strategiesEstablish production AI lifecycle architecture with MLOps principles: automation, monitoring, drift detection, reproducibility, and operational readinessPartner with enterprise architecture/security teams to ensure AI initiatives integrate cleanly with platform governanceProduce executive-ready AI architecture artifacts: target state, integration patterns, governance model, and phased adoption roadmap