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Join us at Barclays as an AI Evaluation & Assurance Architect -We’re looking for a forward-thinking architect to shape how AI is evaluated, governed, and trusted across the enterprise. This is a foundational position within our AI Accelerator, focused on building and scaling frameworks that ensure AI systems are reliable, trustworthy, and defensible. You’ll define evaluation standards, assurance processes, and reference architectures across Generative AI, Agentic AI, and AI/ML—supporting their full lifecycle from design through to live operation. Working across architecture, engineering, risk, and governance, you’ll enable rapid AI adoption while maintaining control, transparency, and confidence.
Job Responsibility:
Shape how AI is evaluated, governed, and trusted across the enterprise
Build and scale frameworks that ensure AI systems are reliable, trustworthy, and defensible
Define evaluation standards, assurance processes, and reference architectures across Generative AI, Agentic AI, and AI/ML
Support their full lifecycle from design through to live operation
Enable rapid AI adoption while maintaining control, transparency, and confidence
Provision of guidance and expertise to engineering teams to ensure alignment with best practices and foster a culture of technical excellence
Contribution to strategic planning by aligning technical decisions with business goals, anticipating future technology trends, and providing insights to optimize product roadmaps
Design and implementation of complex, scalable, and maintainable software solutions, considering long-term viability and business objectives
Mentoring and coaching to junior and mid-level engineers to foster professional growth and knowledge sharing
Collaboration with business partners, product managers, designers, and other stakeholders to translate business requirements into technical solutions and ensure a cohesive approach to product development
Innovation within the organization by identifying and incorporating new technologies, methodologies, and industry practices into the engineering process
Requirements:
Evaluation architecture for probabilistic AI: Design robust evaluation strategies for non-deterministic systems (e.g. LLMs), including test datasets, scoring methods, and release gates
Risk-based assurance design: Translate AI risks into effective controls, covering areas like bias, hallucination, adversarial testing, and data leakage
Operationalisation (LLMOps & monitoring): Embed evaluation into production through continuous monitoring, observability, CI/CD gating, and auditability
Simulation & synthetic data: Create realistic scenarios and stress tests to improve coverage and resilience