2 days ago
Bengaluru, IndiaEntry Level
Responsibilities
- Design and implement single-agent and simple multi-agent workflows for traded-risk teams.
- Lead end-to-end development of agentic AI applications from ideation and data exploration through prototyping and initial deployment.
- Build function-calling and MCP-server integrations connecting LLM agents to internal risk systems.
- Develop RAG pipelines over risk policies, regulatory guidance, and historical incident logs.
- Create prompt templates, system instructions, few-shot examples, and a versioned prompt library.
- Build evaluation harnesses measuring accuracy, hallucination rates, and task completion through automated metrics and human review.
- Implement input/output validation, confidential-data and PII filtering, approval checkpoints, and human escalation paths.
- Monitor production agents for token cost, latency, failure modes, and output-quality drift.
- Document workflows, decision logic, and control points for audit and model-risk review.
- Collaborate with market-risk subject-matter experts to structure manual processes into agent tasks.
Requirements
- Working proficiency in Python, including API integration, asynchronous programming, and data manipulation with pandas and NumPy.
- Hands-on experience building agentic applications through personal projects, hackathons, internships, or professional work.
- Practical experience with an agent orchestration framework such as LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel, or with Claude, OpenAI, or Gemini APIs and function calling.
- Understanding of prompt engineering, including system prompts, structured reasoning prompts, few-shot examples, and JSON or XML output formatting.
- Familiarity with RAG architectures, embeddings, vector databases, chunking strategies, and retrieval evaluation.
- Basic understanding of LLM evaluation, including golden datasets, LLM-as-judge patterns, and regression testing for prompt changes.
- Foundational knowledge of market-risk concepts such as VaR, Greeks, stress testing, and limit frameworks.
- Experience with MCP server development for tool integration.
- Exposure to model-risk management or SR 11-7-style validation frameworks.
- Prior experience in a regulated environment such as banking, insurance, or asset management.
- Background in software engineering, data engineering, or quantitative development.
- FRM Part I or II, CQF, or an equivalent certification in progress or completed.
- Intern candidates may be considered with one year of AI exposure.
Benefits
- Flexible working and opportunities for continuous professional development and career growth.
- Inclusive and diverse workplace with equal-opportunity employment practices.
- This is a fixed-term contract (FTC) role.
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.