Risk Modeling Solutions - Full-stack GenAI - Assistant Vice President

Citi; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Citi's AI Lab in Bengaluru is seeking a Gen AI Lead Engineer to design and implement advanced agentic AI workflows, integrate RAG pipelines, and build scalable backend services. You will champion LLM orchestration, memory, tool use, and evaluation frameworks, collaborating with risk modeling teams to translate business problems into robust AI solutions.

This full-time role requires 6+ years of experience, deep Python development, and expertise with LangChain, LangGraph, Docker/Kubernetes, and

Qualifications

• 6+ years of experience blending software development and data science.
• Expert-level Python and scalable backend/API design (FastAPI).
• Hands-on with multiple agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
• Experience with RAG systems and vector databases (OpenSearch, Pinecone, Weaviate).
• Containerized AI systems on AWS/Azure/GCP; Docker/Kubernetes.
• MLOps: CI/CD, observability, evaluation for LLMs.
• Knowledge of AI risk, safety, and governance.

Responsibilities

• Architect and lead multi-agent AI workflows with advanced reasoning.
• Translate business problems into technical requirements and end-to-end AI solutions.
• Develop Python microservices and REST APIs to expose capabilities.
• Implement RAG pipelines and vector search integrations.
• Containerize services and deploy on Kubernetes with CI/CD.
• Instrument workflows with observability and evaluation harnesses.

Skills

Python

Backend APIs

LangChain/LangGraph

RAG systems

Docker/Kubernetes

MLOps CI/CD

LLM/NLP

AI risk & governance

Tools

OpenSearch

Pinecone

Weaviate

Azure OpenAI/Search

AWS Bedrock

Job description

Risk Modeling Solutions - Gen AI Lead Engineer

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:

• Architect Agentic Systems: Design and lead the implementation of complex, multi-agent AI workflows capable of advanced reasoning, planning, and autonomous execution using frameworks like LangGraph, CrewAI, and Google ADK.
• Solution Design & Development: Translate complex business problems within the risk domain into well-defined technical requirements, and develop robust, end-to-end AI solutions to address them.
• 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.
• Back-end & 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.

Qualifications

• 6+ years of professional experience in a role blending software development and data science/machine learning.
• Expert-level Python development skills and a proven track record of designing and building scalable backend services and APIs (FastAPI preferred).
• Deep, hands-on experience designing and building solutions with multiple agentic frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
• Extensive experience architecting and optimizing RAG systems and integrating with vector databases (e.g., OpenSearch, Pinecone, Weaviate).
• Proven expertise in designing and deploying containerized (Docker/Kubernetes) AI systems on a major cloud platform (AWS, Azure, or GCP).
• Strong experience implementing MLOps principles, including CI/CD, observability, and evaluation frameworks for LLM-based systems.
• In-depth understanding of AI risk, safety, and enterprise governance requirements.
• Strong background in ML, deep learning, and NLP, including Transformer architectures.

Preferred Qualifications

• Experience leading the design of LLM evaluation harnesses for automated release validation.
• Deep experience with Langfuse or similar AI observability and tracing platforms.
• Hands-on expertise with AWS Bedrock, Azure AI Foundry, or GCP Vertex AI.
• Knowledge of GraphRAG patterns and advanced multi-hop retrieval strategies.
• AWS certifications (e.g., Solutions Architect, AI/ML Specialty) or equivalent.
• Background in financial services or another highly regulated industry.

Job Family Group

Risk Management

Job Family

Model Development and Analytics

Time Type

Full time

Most Relevant Skills

Analytical Thinking, Credible Challenge, Data Analysis, Governance, Policy, Procedure, and Regulation, Risk Management Lifecycle.

Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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