Enterprise AI Architect

• The Enterprise AI Architect defines and governs the organizations enterprise-wide AI architecture, enabling secure, scalable, policy-driven, and cost-optimized Artificial Intelligence adoption.
• This role designs the architectural control layers governing model access, agent orchestration, routing logic, data boundaries, observability, and financial oversight across AI workloads.
• The Architect ensures that AI initiatives transition from experimentation into resilient, enterprise-grade production systems.
• The role operates at the intersection of enterprise architecture, Agentic AI systems, governance, cloud platforms, security, and FinOps.

Key Responsibilities

• Enterprise AI Architecture Strategy
• Define and maintain the enterprise AI reference architecture across experience, control, agent, model, data, and observability layers.
• Translate executive AI strategy into scalable, vendor-neutral, modular technical blueprints.
• Ensure hybrid and multi-cloud compatibility aligned with enterprise standards.
• Agentic AI Architecture
• Design and govern enterprise-grade multi-agent orchestration frameworks with controlled autonomy levels and policy-based guardrails.
• Establish agent lifecycle management including registration, monitoring, human-in-the-loop checkpoints, and retirement.
• Define clear architectural boundaries between agent-driven reasoning and authoritative enterprise systems of record.
• Enterprise AI Platform Architecture and Model Orchestration
• Design centralized AI platform architecture governing model access, routing logic, guardrails, and access controls.
• Establish criteria-based model selection and orchestration based on risk, cost, latency, compliance, and performance requirements.
• Ensure production-grade reliability, operational visibility, and controlled separation between enterprise AI systems and experimental environments.
• FinOps and AI Cost Governance
• Establish AI financial governance frameworks including usage metering, cost attribution, and chargeback models.
• Architect cost-performance optimization strategies including intelligent model selection and token consumption controls.
• Partner with Finance and Cloud teams to align AI consumption with enterprise budgeting discipline.
• LLMOps and MLOps
• Define operational standards for model lifecycle management including onboarding, evaluation, versioning, and retirement.
• Implement CI/CD, rollback, monitoring, drift detection, and reliability engineering patterns for AI systems.
• Establish performance benchmarks and service-level governance for AI workloads.
• Data Architecture for AI
• Define integration patterns between AI services and structured and unstructured enterprise data sources.
• Establish governance standards for retrieval, embeddings, contextualization, and secure data access.
• Maintain clear data ownership boundaries aligned with privacy and compliance requirements.
• Enterprise Integration
• Architect scalable integration patterns between AI systems and enterprise platforms such as ERP, CRM, and operational systems.
• Define reusable APIs and service contracts for AI consumption across business units.
• Ensure AI remains advisory where appropriate and governed in high-impact workflows.
• Governance and Risk
• Establish risk classification frameworks and production controls for AI workloads.
• Embed Responsible AI principles including explainability, audit readiness, and compliance alignment.
• Partner with Security, Legal, and Risk functions to institutionalize AI governance standards.