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