AVP - Traded Risk Analysis

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HSBC is one of the largest banking and financial services organisations in the world, with operations in 64 countries and territories. We aim to be where the growth is, enabling businesses to thrive and economies to prosper, and, ultimately, helping people to fulfil their hopes and realise their ambitions.

We are currently seeking an experienced professional to join our team in the role of AVP - Traded Risk Analysis (Contractual for 1 Year)

In this role, you will:

- Own the technical architecture for agentic AI workflows across the Market Risk function: agent orchestration patterns, multi-agent coordination, tool/permission design, and human-in-the-loop checkpoints.

- Set the evaluation and governance framework: define what "production-ready" means for an agent handling risk data, including accuracy thresholds, red-teaming for prompt injection and jailbreaks, and continuous monitoring standards.

- Lead model risk and regulatory engagement — partnering with Model Risk Management, Compliance, and Internal Audit to get agentic systems validated and approved for use in regulatory-adjacent workflows (e.g., VaR backtesting exception analysis, regulatory report drafting, RWA/limit reconciliation).

- Design fallback, escalation, and kill-switch mechanisms so that any agent making or influencing a risk decision has a clear, auditable human checkpoint and can be safely disabled.

- Architect multi-agent systems (e.g., a supervisor agent delegating to specialist sub-agents for data retrieval, calculation checks, narrative generation, and anomaly flagging) with attention to cost, latency, reliability, and failure isolation.

- Mentor a team of AI engineers/analysts, review their designs, prompts, and evaluation results; set engineering and documentation standards.

- Own the vendor and platform strategy: evaluate build-vs-buy decisions across LLM providers, agent frameworks, and enterprise AI platforms, factoring in data residency, security, and cost.

- Partner directly with senior Market Risk stakeholders to identify high-value automation candidates (e.g., daily risk commentary, limit exception triage, ad-hoc scenario analysis, regulatory query response) and prioritize a roadmap.

- Define cost governance for LLM usage (token budgets, model selection by task complexity) at a portfolio level, not just per-workflow.

- Represent the function to senior management and, where relevant, to regulators, on how AI is being used responsibly within risk processes.

To be successful you will:

- Demonstrated ownership of agentic or LLM-based systems in production, not just prototypes — including having handled real incidents (hallucination, drift, cost overrun, or a failed evaluation gate) and the process improvements that followed.

- Deep, practical fluency with agent orchestration frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent) and with designing multi-agent systems, not just single-agent chat flows.

- Experience designing RAG and knowledge-grounding architectures at scale, including retrieval quality tuning, source attribution, and strategies to minimize hallucination on factual/numeric outputs.

- Working knowledge of the regulatory and model-risk landscape for AI in banking (e.g., how model validation frameworks are being adapted for generative/agentic AI; explainability expectations; data governance).

- Proven ability to architect for reliability and cost — caching, model routing (using smaller/cheaper models for simple sub-tasks), token budgeting, and latency management in multi-step agent chains.

- Solid software engineering foundation: API design, cloud infrastructure (AWS/Azure/GCP), CI/CD for prompt/agent versioning, and observability tooling for non-deterministic systems. People leadership experience — able to set technical direction for and review the work of less experienced AI engineers.

- Excellent stakeholder management — able to translate agentic AI capability and risk in language that resonates with senior risk managers, compliance, and technology leadership. Awareness of responsible-AI concepts relevant to finance: hallucination risk, data leakage, model explainability, and the need for human oversight in high-stakes decisions.

- Substantive market risk domain expertise — VaR methodologies, sensitivities/Greeks, stress testing, limit frameworks, regulatory capital (FRTB, SIMM) — gained through direct experience in a market risk function.

- FRM (Part I & II), PRM, CQF, or CFA — as evidence of quantitative risk fluency, thoughd irect experience can substitute.

- Prior experience within a formal Model Risk Management or model validation function.

You’ll achieve more at HSBC

Hsbc.Com/Careers

HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”

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