Risk Modeling Solutions - Full-stack GenAI - Intermediate Analyst

Full-Stack Gen AI Engineer – Model/Anlys/Valid Intmd Anlyst (C11)

The AI Lab is the engineering core of our Risk Modeling Solutions (RMS) team, focused on integrating Gen AI solutions into our risk management framework. The team is responsible for building and deploying practical, high-impact applications that combine deep quantitative analysis with cutting-edge AI. These solutions enhance analytical decision-making, automate complex reporting, and create significant operational efficiencies for the business.

The responsibility includes but not limited to the following activities:

- AI Workflow Development: Implement end-to-end agentic AI workflows using frameworks like LangGraph, CrewAI, and AutoGen, focusing on reasoning, tool use, and memory.

- LLM Orchestration: Build and optimize retrieval pipelines, memory layers, and tool-use sequences using frameworks like LangChain.

- Backend & API Engineering: Develop robust, scalable Python-based microservices and REST APIs using FastAPI to expose AI capabilities.

- RAG Implementation: Construct and refine Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding, and vector search integration with databases like Azure AI Search or Pinecone.

- Containerization & Deployment: Package AI services using Docker and deploy them on Kubernetes, contributing to CI/CD pipelines for smooth and reliable releases.

- Observability & Evaluation: Instrument AI workflows using platforms like Langfuse for tracing and debugging. Implement and maintain evaluation harnesses to ensure model quality and performance.

- Engineering Excellence: Actively participate in code reviews, contribute to technical documentation, and collaborate with team members to uphold high engineering standards.

Qualifications:

- 4+ years of professional experience in a role blending software development and data science/machine learning.

- Strong Python development expertise for AI systems and backend services.

- Experience building production APIs with FastAPI and microservices architecture.

- Proficiency with Docker and Kubernetes for containerized deployments.

- Hands-on experience with agentic AI or LLM orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, LlamaIndex).