Quant / Agentic AI Developer - AVP

Job Description Purpose of the role To provide quantitative and analytical expertise to support trading strategies, risk management, and decision-making within the investment banking domain, applying quantitative analysis, mathematical modelling, and technology to optimise trading and investment opportunities. Accountabilities Development and implementation of quantitative models and strategies to derive insight into market trends and optimize trading decisions, pricing, and risk management across various financial products and markets. Working closely with sales teams to identify clients' needs and develop customised solutions. In-depth research, data analysis, and statistical modelling to derive insights into market trends, pricing, and risk dynamics. Provide front office infrastructure support though ownership and maintenance of analytical libraries. Provision of expertise on quantitative methodologies, technological advancements, and industry best practices to drive innovation within the trading environment. Assistant Vice President Expectations To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions. Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others. OR for an individual contributor, they will lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes. Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues. Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda. Take ownership for managing risk and strengthening controls in relation to the work done. Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function. Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy. Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, etc).to solve problems creatively and effectively. Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience. Influence or convince stakeholders to achieve outcomes. All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave. Join us at Barclays as a Quant / Agentic AI Developer in Global Markets, where you will play a key role in building a new Agentic AI capability. Working alongside traders, quantitative researchers, ML engineers and data engineers, you will design and deliver production-grade AI agents that automate and augment complex business workflows. You will build solutions that reason over enterprise knowledge, interact securely with tools and systems, and operate reliably from prototype through to production. You will also help make existing technology and data platforms AI-ready, develop robust knowledge and retrieval capabilities, and ensure solutions are secure, observable, cost-efficient and appropriately governed. To be successful as a Quant / Agentic AI Developer, you must have the following essential skills: Proven experience taking at least one LLM or Agentic AI application from prototype into production, including tool calling, orchestration, evaluation, monitoring and iteration. Considerable Python, ML and software engineering skills, with experience of testing, CI/CD, Git, containerisation and cloud-based AI workloads. Considerable knowledge of LLMs and NLP, including prompt/context engineering, embeddings, structured outputs, retrieval and evaluation. Experience building RAG and enterprise knowledge solutions, including document ingestion, chunking, vector databases, hybrid search, re-ranking, metadata and access-controlled retrieval. Experience with modern agent frameworks and technologies such as LangGraph, Claude Agent SDK, OpenAI Agents SDK, Strands/Bedrock AgentCore, Microsoft Agent Framework or equivalent, with the ability to work effectively with technical and business stakeholders. Master's degree in a Science, Technology, Engineering and Mathematics (STEM) field (or equivalent industry experience). Some other highly valued skills may include: Experience designing multi-agent architectures, MCP/tool integrations, shared state and agent-to-agent collaboration. Knowledge of LLM evaluation, observability, guardrails and cost optimisation, using technologies such as Langfuse, LangSmith, OpenTelemetry or Bedrock Guardrails. Experience with AWS AI services, including Amazon Bedrock, SageMaker, Lambda, EKS or S3. Experience with AI-assisted DevOps, knowledge graphs/GraphRAG, long-term agent memory, model fine-tuning or inference technologies. Experience within financial services or Global Markets, including an understanding of model risk, controls and AI governance. You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills. This role is based in London. #LI-Hybrid