Description

Key Responsibilities

 Design develop enhance and maintain Pythonbased GenAI services agent workflows MCP integrations and reusable platform capabilities
 Build MCP servers clients tools resources prompts schemas and authorization patterns for internal and thirdparty systems
 Implement agent orchestration flows including tool calling function calling workflow execution retrieval and output generation
 Integrate internal and external data sources such as financial data providers SEC filings web search enterprise repositories and banking data services into agent workflows
 Design and implement RAG pipelines covering ingestion chunking embeddings retrieval ranking answer synthesis and citations
 Develop productiongrade backend services and APIs using Python FastAPI async programming patterns and secure service integration practices
 Implement authentication authorization entitlement checks and secure access patterns for userspecific consumption of MCPenabled services
 Perform testing debugging prompt evaluation modeloutput validation logging monitoring and operational telemetry for GenAI components
 Work closely with product owners business analysts architects cloud engineers governance teams and bankers to convert requirements into productionready AI capabilities
 Maintain clear documentation of technical design assumptions interfaces limitations failure modes and operating considerations

Essential  Basic Qualifications

 To be successful in this role you should have
 Strong handson experience in Python for backend ML or GenAI application development
 Proven experience building GenAI applications LLM workflows agentic systems or AIenabled production services
 Solid understanding of MCP concepts and practical implementation patterns for tools resources prompts servers and clients
 Experience with LLM orchestration frameworks such as LangChain LangGraph or similar
 Strong understanding of RAG vector search embeddings retrieval quality citations and grounding patterns
 Experience developing APIs and backend services using FastAPI REST APIs async Python and servicetoservice integration patterns
 Working knowledge of Git modern versioncontrol practices CICD workflows and test automation
 Experience deploying or integrating containerized services in a cloud environment preferably AWS
 Good understanding of authentication authorization secrets handling entitlements and enterprise security patterns
 Ability to independently debug complex distributed systems across APIs data flows cloud services prompts and model outputs
 Prior experience in banking financial services capital markets or other regulated technology environments
 Bachelor degree in a quantitative or technical discipline such as Computer Science Engineering AI or equivalent experience

Desirable  Good to Have Skills

 Handson experience with GenAI capabilities on AWS including Bedrock Claude models embedding services or equivalent managed AI services
 Experience integrating financial data providers such as FactSet Bloomberg SP Capital IQ Refinitiv Fitch or similar
 Experience implementing enterprise web search document intelligence or searchgrounded AI workflows
 Experience with vector databases or search platforms such as OpenSearch pgVector Pinecone Weaviate Chroma or similar
 Familiarity with OpenTelemetry structured logging promptversion tracking AI evaluation frameworks and production observability
 Understanding of model risk management AI governance data privacy explainability and controls in regulated firms
 Experience building outputgeneration capabilities such as Word PowerPoint PDF briefing packs or documentgeneration workflows
 Exposure to Investment Banking workflows such as IPO prospectus preparation shareholder activism research ECM DCM MA or advisory processes

Key Skills

Education

Bachelor's degree