Sr. Gen AI Python Engineer

Role : Sr. Gen AI Python Engineer

Duration : Long Term Contact

Location : Hanover NJ Hybrid

Key Responsibilities

Design develop enhance and maintain Python based 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 production grade 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 MCP enabled services

Perform testing debugging prompt evaluation model output 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 production ready 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 hands on experience in Python for backend ML or GenAI application development

Proven experience building GenAI applications LLM workflows agentic systems or AI enabled 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 service to service integration patterns

Working knowledge of Git modern version control 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

Hands on 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 pg Vector Pinecone Weaviate Chroma or similar

Familiarity with OpenTelemetry structured logging prompt version 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 document generation workflows

Exposure to Investment Banking workflows such as IPO prospectus preparation shareholder activism research ECM DCM MA or advisory processes

Other Skills Attributes

Strong problem solving mind set with the ability to operate independently in a fast moving delivery environment

Excellent communication skills and the ability to collaborate with technical and nontechnical stakeholders

Pragmatic engineering mind set with strong ownership accountability and focus on production readiness

Comfortable working across global teams architecture governance security reviews and enterprise delivery processes

Willingness to mentor engineers share knowledge and contribute to reusable GenAI engineering patterns