AI Architect

Job Description & Summary
At PwC, we help clients build trust and reinvent so they can turn complexity into competitive advantage. We’re a tech-forward, people-empowered network with more than 370,000 people in 149 countries. Across audit and assurance, tax and legal, deals and consulting we help build, accelerate and sustain momentum.
The role:
As an AI Architect, you will design and guide production-ready Generative AI solutions for clients, from early discovery through implementation. This role combines hands-on architecture, technical leadership, and client collaboration. You will help teams choose where Generative AI adds value, build secure and scalable solutions, and measure their quality and cost in production.
Your responsibilities will include:
Solution Architecture & Delivery
Lead the architecture and delivery of Generative AI solutions, guiding development teams on relevant tools, frameworks, and platforms.
Translate business requirements into robust application, service, and target-state architectures, including reusable RAG, agent orchestration, prompt, and evaluation patterns.
Own scalability, performance, latency, observability, security, and cost optimisation across AI solutions.
Lead technical design and code reviews and guide delivery against client needs and quality standards.
Define the right adaptation approach for each use case—prompting, RAG, fine-tuning, or distillation—including when Generative AI is not appropriate.
AI Quality, Security & Operations
Own evaluation strategy, including golden datasets, offline and online evaluation, human-calibrated LLM-as-judge, and CI regression testing.
Set retrieval quality standards for chunking, hybrid search, reranking, query rewriting, and permission-aware retrieval, using measured recall and precision.
Define AI security, privacy, Responsible AI, and governance controls, including prompt injection, data exfiltration, jailbreak, sandboxing, least-privilege identity, and supply-chain risks.
Own solution economics through model routing, caching, batching, capacity planning, and defensible cost-per-transaction estimates.
Support enterprise architecture standards and help assess technology solutions.
Leadership, Clients & Practice
Supervise vendor developers and mentor engineers and junior architects.
Support business development by identifying and researching opportunities with new and existing clients.
Improve internal design and development practices and maintain documentation.
Build internal relationships, strengthen the firm's reputation, and develop your skills in line with company and team priorities.
Take on additional responsibilities relevant to the role and agreed with your manager.
What You'll Bring
Experience
5+ years of experience in agile product delivery.
Hands-on experience designing and delivering production software solutions at scale.
Demonstrated expertise in Generative AI solution architecture.
You have personally delivered at least one Generative AI system to production and can explain its architecture, failure modes, operating costs, and lessons learned.
Education & Certifications
Bachelor's or Master's degree in Computer Science or Engineering, or equivalent practical experience.
Relevant Microsoft Azure certification, such as Azure Solutions Architect Expert or Azure Developer Associate, is preferred.
Technical Skills & Specialized Knowledge
Strong hands-on experience with Generative AI architecture is essential. Experience across several relevant technologies is expected; expertise in every named tool is not required.
Generative AI & Agent Architecture
Large language models such as GPT-4, Gemini, and Llama.
RAG, agent orchestration, and Microsoft Copilots using platforms or frameworks such as Azure OpenAI Service, Semantic Kernel, LangChain, AWS Bedrock, or Google Vertex AI.
Vector search and retrieval engineering, including chunking, hybrid search, reranking, query rewriting, metadata filtering, and permission-aware retrieval.
Production AI agents, including tool calls, state and memory management, durable execution, approval controls, retries, idempotency, and cost safeguards.
Structured generation, including schema-enforced outputs, validation and repair, deterministic fallbacks, and graceful degradation.
Cloud, Software & Data Engineering
Cloud-native Azure architecture, including Azure Machine Learning, Azure AI services, and appropriate infrastructure and platform services.
Service-oriented, event-driven, and microservices architectures.
Relational and NoSQL data stores, such as Microsoft SQL Server and MongoDB, with sound technology-selection judgement.
Containers and orchestration using Docker and/or Kubernetes.
DevOps and delivery practices, including CI/CD, automation, environment management, and tools such as Azure DevOps, GitHub, or Jira.
Working knowledge of user-centred product practices, including user stories, personas, and prototyping.
Evaluation, Operations & Security
AI evaluation using golden datasets, LLM-as-judge, and prompt and model regression testing.
LLMOps and observability, including tracing, token and cost attribution, prompt and model versioning, trace replay, and quality-drift monitoring, using tools such as LangSmith, Langfuse, or Azure Monitor.
Model economics, including routing, cascading, caching, batching, and provisioned-versus-consumption capacity planning.
AI security, including prompt injection, data exfiltration, jailbreak resistance, sandboxing, least-privilege identities, supply-chain risk, and the OWASP Top 10 for LLM Applications.
Nice to Have
Model Context Protocol and agent interoperability standards.
Model adaptation beyond prompting and RAG, including fine-tuning, LoRA/PEFT, and distillation.
Multimodal AI, including document intelligence, vision, speech, or voice agents.
AI regulation and assurance frameworks, including the EU AI Act, ISO/IEC 42001, and NIST AI RMF.
Consulting or professional-services experience with external clients.
Attributes & Soft Skills
Strategic, analytical problem-solving with attent