Enterprise Architect + AI Focus

Job Description
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

1. Background
As organizations evaluates and scales 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.

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

3. 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 state, integration patterns, governance model, and phased adoption roadmap