Grant Thornton is building an AI Factory to deliver production‑grade, agentic AI solutions that generate measurable business outcomes while meeting enterprise standards for trust, security, and governance. As a Lead AI Architect, you will serve as the technical authority within an AI Pod, responsible for designing, governing, and scaling agentic systems from concept through production. This role sits at the intersection of AI engineering, enterprise architecture, and responsible AI. You will define how agents think, act, integrate, and fail safely ensuring solutions are robust, observable, and fit for real‑world operations. Responsibilities Own the end‑to‑end architecture for agentic AI solutions, from design through production Define and implement agentic patterns, including: Planner / Executor / Validator agents Tool‑using and multi‑agent orchestration Memory, retrieval (RAG), and context strategies Ensure agent behavior is: Bounded Observable Recoverable in failure scenarios Platform & Integration Design Select and standardize on appropriate platforms and services (e.g., Azure‑based AI stacks) Design integration patterns for: Enterprise systems (ERP, CRM, case management) APIs and event‑driven workflows Human‑in‑the‑loop escalation paths Partner with Automation and Integration Engineers to ensure agents can execute actions, not just generate responses Enterprise Readiness & Non‑Functional Requirements Define and enforce non‑functional requirements, including: Security, identity, and access control Data privacy and handling constraints Latency, reliability, and cost controls Ensure solutions are auditable, traceable, and aligned with enterprise risk expectations Design for scale, reuse, and long‑term maintainability across AI Pods Evaluation, Monitoring & Guardrails Establish evaluation frameworks for: Accuracy and quality Hallucination detection Drift and degradation over time Define monitoring and observability standards: Model and prompt performance Cost‑to‑serve and usage patterns Failure and escalation metrics Embed Responsible AI and safety controls by design, not as after‑the‑fact reviews Collaboration & Enablement Partner closely with: AI Product Leads on use‑case framing and acceptance criteria AI Engineers on implementation and optimization Central Platform & Trust teams on standards and guardrails Contribute to reusable patterns, reference architectures, and playbooks within the AI Factory Skills And Experience 8+ years in software architecture, AI engineering, or platform engineering Hands‑on experience designing and deploying AI systems into production Demonstrated ability to operate as a technical authority across multiple teams or initiatives Experience working in enterprise or regulated environments Agentic & AI Expertise Deep understanding of: Generative AI and LLM behavior Agentic architecture and orchestration patterns Prompt engineering as a software discipline Practical experience implementing: Tool calling and action frameworks Memory and retrieval systems (RAG) Multi‑step reasoning and control flows Strong grasp of AI failure modes and mitigation strategies Technical Skills Proficiency in Python and/or TypeScript Experience with: AI/LLM SDKs and orchestration frameworks API‑first and event‑driven architectures CI/CD for AI workloads Familiarity with cloud‑native architecture patterns (preferably Azure) Preferred Qualifications Experience designing AI solutions with: Human‑in‑the‑loop controls Regulatory or audit requirements Background in MLOps, platform engineering, or large‑scale distributed systems Exposure to Responsible AI, model risk management, or AI governance frameworks #J-18808-Ljbffr