Enterprise AI Architect

Job Description & Summary The opportunity Design end-to-end, client-specific AI architectures that integrate agents, models, enterprise data, applications, identity and controls across cloud and on-premises environments. What you will be doing ·        Translate business and product requirements into target architectures and implementation decisions. ·        Design agent, RAG, model-routing, integration, API, identity and human-in-the-loop patterns. ·        Define hybrid deployment patterns that account for residency, latency, security, performance and cost constraints. ·        Evaluate technology choices and document architecture decisions, trade-offs and non-functional requirements. ·        Provide technical assurance throughout delivery and support production-readiness reviews. ·        Collaborate with existing governance, Responsible AI, cyber, privacy and sector specialists. What we need from you ·        8+ years in solution, enterprise, cloud or AI architecture. ·        Strong knowledge of generative AI, agentic systems, data platforms, integration and distributed applications. ·        Experience designing hybrid cloud and on-premises solutions. ·        Ability to communicate architecture choices to executives, engineers, security teams and business owners. Relevant AI technologies and tooling ·        Hands-on architecture experience with at least two agent orchestration approaches, including LangChain and LangGraph, Microsoft Agent Framework or Semantic Kernel, OpenAI Agents SDK, AutoGen, CrewAI, or equivalent frameworks. ·        Ability to design deterministic and agentic workflows, single-agent and multi-agent patterns, durable state, memory, tool calling, hand-offs, human approval, fallback and exception handling. ·        Strong knowledge of RAG and knowledge architectures, including embedding models, vector and hybrid search, reranking, metadata filtering, semantic layers, knowledge graphs, context management and retrieval evaluation. ·        Experience designing model-agnostic and multi-model architectures across managed and self-hosted models, including model routing, gateways, prompt and policy layers, structured outputs, caching and latency or cost trade-offs. ·        Practical knowledge of MCP and API-based tool integration, event-driven architecture, identity delegation, secrets management, auditability and zero-trust patterns for agents. ·        Experience producing architecture artefacts for hybrid deployment using cloud AI platforms, containers and Kubernetes, private networking, on-premises data sources and locally hosted inference where required. Measures of success ·        Architecture quality and stakeholder approval ·        Reuse of proven patterns ·        Reduction of technical risk and rework ·        Production scalability, security and operability ·        Clarity and timeliness of architecture decisions Key interfaces ·        Other members of the AI Transformation & Agentic Systems Practice ·        PwC sector, functional, cloud, cyber, risk, Responsible AI and change specialists ·        Client business owners, product owners, technology teams and operational users ·        Technology alliance and implementation partners where relevant Contribution to the practice ·        Support proposals, client workshops and market development appropriate to seniority. ·        Contribute reusable methods, patterns, code, assets and lessons learned. ·        Coach colleagues and participate in the capability’s continuous learning agenda. ·        Uphold PwC quality, independence, confidentiality and risk-management requirements. #LI-BS1 #LI-Hybrid