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.