AI Engineer- Traded Risk
HSBC Holdings plc (company site, opens in a new tab)BengaluruOn-siteDirect career pageEst. 25–45 LPA
Posted 1d ago
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Job description
Structured overview of role & requirements
About This Role
- Design and implement single-agent and simple multi-agent AI workflows for traded risk management, using frameworks like LangGraph, LangChain, AutoGen, and CrewAI.
- Lead end-to-end development of agentic AI applications including prototyping, deployment, and integration with internal risk systems.
- Monitor, evaluate, and document AI agents for accuracy, compliance, cost, and performance, collaborating with market risk SMEs to translate manual risk processes into automated agent tasks.
Minimum Requirements
- Proficient in Python programming, including API integration, asynchronous programming, and data handling (pandas/numpy).
- Experience building agentic AI applications using at least one agent orchestration framework (LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel) or direct use of OpenAI/Claude/Gemini APIs.
- Foundational market risk knowledge (VaR, Greeks, stress testing) plus completion or progress on FRM Part I/II, CQF, or equivalent certification.
- Prior exposure to regulated environments (banking, insurance, asset management) or experience in software engineering, data engineering, or quant development.
- Work Experience Required: Interns with 1 year of AI exposure considered; otherwise, prior relevant experience explicitly indicated.
Ideal Candidate Profile
- Technically strong candidate with hands-on experience in AI agent orchestration and integration in regulated financial contexts.
- Comfortable working at the intersection of AI engineering and market risk domain, capable of translating complex risk policies into AI-driven workflows.
- Experienced in designing AI evaluation frameworks, operating within model risk management standards such as SR 11-7 and familiar with MCP server development for tool integration.