Senior Manager, Responsible AI Solution Architect

PwC

Senior Manager and Responsible AI Solution Architect at PwC leading the design and delivery of trusted AI solutions. Shaping the Responsible AI, Ethics, Security and Trust agenda across various sectors.

Posted 7/7/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudSDLC

About the role

Key responsibilities & impact

architect end-to-end AI / GenAI / agentic solutions (from data and identity through model integration, orchestration, deployment, monitoring and incident response), embedding PwC’s “Trust by Design” architecture patterns to ensure systems are secure, governed, transparent and resilient
develop and mature PwC’s “Trust by Design” reference architectures and patterns for GenAI and agentic AI, embedding: safety and policy controls (guardrails, content safety, prompt-hardening), transparency and auditability (logging, traceability, lineage), privacy and security controls (data minimisation, encryption, key management), and human-in-the-loop and escalation mechanisms
translate regulatory, policy, privacy and ethical requirements into concrete technical controls embedded across: SDLC and secure-by-design practices, data lifecycle and data governance, model lifecycle (training, fine-tuning, evaluation, release, monitoring)
partner with cyber and resilience specialists to advance AI threat modelling, prompt injection and data exfiltration mitigations, adversarial testing, and model assurance approaches
define and lead AI assurance strategies across testing, validation, monitoring and control effectiveness across classical ML and GenAI
oversee development of test plans and evaluation frameworks including: functional performance testing (accuracy/task success), safety testing (toxicity, bias, harmful capability), robustness testing (adversarial prompts, jailbreak attempts), privacy and leakage testing, explainability and transparency checks, and post-deployment monitoring (drift, incidents, regressions)
shape approaches to model validation and independent assurance, including documentation, governance evidence packs, and audit-ready artefacts
act as technical delivery lead / solution architect on flagship engagements, owning architecture decisions, quality, and delivery outcomes
lead multidisciplinary teams across RAI, engineering, cyber, data governance and risk, bringing structure, pace and clarity to complex delivery
codify delivery experience into reusable assets (frameworks, playbooks, accelerators, reference architectures) to scale PwC’s methodology and differentiation
contribute to the UK Responsible AI go-to-market strategy, shaping priority offerings, target sectors, alliance motions and client propositions
lead and contribute to technical thought leadership on GenAI, agentic systems, LLM assurance, AI security and governance, staying at the frontier of emerging techniques
mentor and upskill practitioners through internal capability building, training and coaching
build, lead and inspire diverse teams, fostering a culture of ethical innovation, engineering excellence and continuous learning
collaborate with global PwC teams to scale Responsible AI capabilities across the network and share repeatable assets

Requirements

What you’ll need

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
Experience in consulting or industry, including leadership of complex AI/data/analytics/model risk initiatives
Strong stakeholder influence: able to advise executives and risk leaders and translate complex technical issues into business and regulatory implications
Commercial acumen: experience shaping proposals, leading workstreams, and supporting large engagements alongside Partners

Benefits

Comp & perks

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
six volunteering days a year and much more

ATS Keywords

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Hard Skills & Tools

AI Governance FrameworksModel Risk ConceptsAI EthicsTesting and Validation ApproachesData ProtectionAdversarial TestingFine-Tuning TechniquesMonitoring and Incident ManagementDesigning Enterprise-Grade AI SystemsOrchestration Patterns

Soft Skills

Stakeholder InfluenceLeadershipMentoringCollaborationCommercial Acumen

Certifications

NIST AI RMFISO/IEC 42001