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Embark on a transformative journey as an AVP ML and GenAI Model Validation at Barclays, where you'll spearhead the evolution of our digital landscape, driving innovation and excellence. You'll harness cutting-edge technology to revolutionize our digital offerings, ensuring unapparelled customer experiences.
Job Responsibility:
Validation of models for their intended use and scope, commensurate with the complexity and materiality of the models
Approval or rejection of a model or usage based on assessment of the model’s conceptual soundness, performance under intended use and the clarity of the documentation of the model’s inherent risks, limitations and weaknesses
Assessment of any compensating controls used to mitigate Model risk
Documentation of validation findings and recommendations in clear and concise reports, providing actionable insights for model improvement
Evaluation of the coherence of model interactions and quality of Large Model Framework aggregate results that generate output for regulatory submissions or management decision making and planning
Design of the framework and methodology to measure and, where possible, quantify model risk, including the assessment of framework level uncertainty
Requirements:
Highly numerate, evidenced by an Honours degree, master’s, or PhD (or equivalent) in a quantitative discipline such as Computer Science, Mathematics, Physics, Operational Research, Economics, Engineering, or Finance
Experience developing, validating, or independently reviewing best‑in‑class tools and AI/ML models for large financial institutions or comparable regulated environments, with the ability to effectively challenge and improve model methodologies and model management processes
Strong analytical and technical expertise in AI/ML modelling, including experience with ML algorithms (e.g., XGBoost, LightGBM, Random Forest), NLP models, and deep learning frameworks (e.g., TensorFlow, PyTorch, Keras)
Experience developing or validating AI/ML models on large datasets in Hadoop/AWS environments
Designed, fine‑tuned, and shipped Generative AI models - transformer‑based LLMs, RAG systems for grounded retrieval, and GANs-using PyTorch/TensorFlow and Hugging Face, including prompt engineering, RLHF, and rigorous evaluation of GenAI models
Hands‑on experience with Generative AI technologies, including Large Language Models, Transformers, retrieval‑augmented generation (RAG), and prompt‑based systems
familiarity with LangChain and LangGraph
Experience designing, developing, validating, or reviewing agent‑based AI architectures, including multi‑agent orchestration, task planning, memory management, and safeguard mechanisms
Sound understanding of financial services regulatory expectations and model risk guidance (e.g., SR 11‑7, SS1/23, IFRS 9, EU AI Act etc.)
Strong communication and influencing skills, with the ability to produce high‑quality written material for both technical and senior non‑technical audiences
Highly organised, with strong attention to documentation quality, governance requirements, and follow‑through on actions
Nice to have:
Experience developing or validating customer‑facing or material GenAI/agentic AI use cases, including chatbots, document generation, summarisation, and decision‑support tools
Prior exposure to model risk management activities such as inception validation, periodic validation, annual reviews, and material model change assessments
Familiarity with Responsible AI practices, including fairness, bias detection and mitigation, explainability, human‑in‑the‑loop controls, and broader ethical AI considerations
Exposure to agentic AI systems, orchestration frameworks, and multi‑model workflows
Relevant certifications or formal training in Generative AI, Large Language Models (LLMs), or Machine Learning
Ability to work effectively within a high‑performing, globally‑distributed team, and to collaborate across diverse technical and control functions
What we offer:
Hybrid working
Modern workspaces, collaborative areas, and state-of-the-art meeting rooms
Facilities include wellness rooms, on-site cafeterias, fitness centers, and tech-equipped workstations
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