14 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Own the technical architecture for agentic AI workflows across Market Risk, including agent orchestration, multi-agent coordination, tool and permission design, and human-in-the-loop checkpoints.
- Define production-readiness, evaluation, governance, red-teaming, and continuous-monitoring standards for AI systems handling risk data.
- Lead model-risk and regulatory engagement with Model Risk Management, Compliance, Internal Audit, senior stakeholders, and regulators where relevant.
- Design auditable fallback, escalation, and kill-switch mechanisms for agents that make or influence risk decisions.
- Architect multi-agent systems for data retrieval, calculation checks, narrative generation, and anomaly flagging with attention to cost, latency, reliability, and failure isolation.
- Mentor AI engineers and analysts, review designs, prompts, and evaluation results, and establish engineering and documentation standards.
- Own vendor and platform strategy, including build-versus-buy decisions across LLM providers, agent frameworks, and enterprise AI platforms.
- Partner with Market Risk stakeholders to identify automation opportunities and prioritize the AI roadmap.
- Define portfolio-level LLM cost governance through token budgets and task-appropriate model selection.
- Represent responsible AI usage in risk processes to senior management and relevant regulators.
Requirements
- Production ownership of agentic or LLM-based systems, including experience handling incidents such as hallucination, model drift, cost overruns, or failed evaluation gates.
- Deep practical fluency with agent orchestration frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent, and experience designing multi-agent systems.
- Experience designing RAG and knowledge-grounding architectures at scale, including retrieval-quality tuning, source attribution, and hallucination reduction for factual or numeric outputs.
- Working knowledge of AI regulatory and model-risk practices in banking, including model validation, explainability, and data governance.
- Experience architecting for reliability and cost through caching, model routing, token budgeting, and latency management.
- Solid software engineering foundation covering API design, cloud infrastructure, versioning of prompts and agents, and observability for non-deterministic systems.
- People leadership experience setting technical direction and reviewing the work of less experienced AI engineers.
- Strong stakeholder management skills with senior risk managers, compliance teams, and technology leadership.
- Awareness of responsible-AI risks including hallucinations, data leakage, model explainability, and human oversight.
- Substantive market-risk expertise covering VaR, sensitivities and Greeks, stress testing, limit frameworks, and regulatory capital such as FRTB and SIMM.
- Prior experience in a formal Model Risk Management or model-validation function.
- FRM Parts I and II, PRM, CQF, or CFA is valued as evidence of quantitative risk fluency; direct experience may substitute.
Benefits
- One-year contractual engagement.
- Flexible working and opportunities for continuous professional development and career growth.
- Inclusive and diverse workplace with equal-opportunity employment practices.
Tech Stack
Categories
About HSBC
HSBC is a global bank that provides retail, commercial and investment banking, payments, and wealth management to individuals, SMEs, and multinationals. It earns fees, interest income, and trading revenues through branches and digital platforms across major markets. Headquartered in London, it serves over 40 million customers in 58 countries and is listed in London, Hong Kong, New York, and Bermuda.