Senior Manager and Responsible AI Solution Architect at PwC shaping Responsible AI and leading trusted AI solution architecture across UK sectors.
Responsibilities
shape and deliver PwC’s Responsible AI, Ethics, Security and Trust agenda
lead the architecture and delivery of trusted AI solutions across GenAI and agentic platforms
help clients move from principles to production by designing secure, governed and observable AI systems
lead the design and delivery of trusted, secure and compliant AI systems across GenAI and emerging agentic platforms
translate Responsible AI principles and regulatory requirements into production ready architectures
architect end-to-end AI / GenAI / agentic solutions (from data and identity through model integration, orchestration, deployment, monitoring and incident response)
develop and mature PwC’s “Trust by Design” reference architectures and patterns for GenAI and agentic AI
translate regulatory, policy, privacy and ethical requirements into concrete technical controls embedded across
Requirements
Deep expertise in Responsible AI principles and operating models, including design or assessment of governance/control frameworks aligned to recognised standards (e.g., NIST AI RMF, ISO/IEC 42001)
Strong understanding of UK/EU AI regulatory landscape (including EU AI Act), data protection, model risk concepts, and AI ethics
Proven experience as a solution architect / technical architect designing and implementing enterprise-grade AI systems
Strong understanding of GenAI architectures (RAG, tool use, function calling, agents, orchestration patterns), including failure modes and risk controls
Hands-on or architecture-level experience with cloud AI platforms and services, ideally across Azure (e.g., Azure AI / Azure OpenAI, Prompt Flow, AML, Purview, Sentinel), AWS (e.g., Bedrock, SageMaker, IAM/KMS, CloudWatch), and other common data platforms, API management, and identity/access patterns
Familiarity with AI enabling technologies and ecosystems: vector databases, embedding pipelines, feature stores, model registries, prompt/trace observability, CI/CD for ML/LLM systems
Demonstrated ability to design and lead testing and validation approaches for AI systems, including GenAI safety testing, adversarial testing/red teaming, monitoring and incident management
Working knowledge of emerging methods such as fine-tuning (e.g. parameter-efficient approaches), evaluation harnesses, and measurement of risk/quality
Benefits
empowered flexibility and a working week split between office, home and client site
private medical cover and 24/7 access to a qualified virtual GP
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