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

Citi

Full-Stack GenAI Lead Engineer in Risk Modeling Solutions team, integrating AI solutions into risk management framework. Building and deploying applications enhancing decision-making and efficiencies in the business.

Posted 7/16/2026full-timeBangalore • 🇮🇳 IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing complex AI workflows using frameworks like LangGraph and CrewAI, with a strong focus on Python development, containerization, and MLOps principles. Proven ability to design scalable backend services and integrate advanced retrieval systems within the risk domain.

Highest-signal resume keywords

Expert-Level Python DevelopmentAI Workflow DevelopmentRAG ImplementationContainerization & DeploymentMLOps Principles

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Applicant Tracking System Keywords

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Hard Skills

PythonFastAPILangGraphCrewAILangChainAutoGenDockerKubernetesMLOpsNLP

Tools & Technologies

Azure AI SearchPineconeLangfuseAWS BedrockAzure AI FoundryGCP Vertex AI

Certifications & Qualifications

AWS Solutions ArchitectAWS AI/ML Specialty

Industry Keywords

AI RiskSafetyEnterprise GovernanceFinancial ServicesRegulated Industry

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesMicroservicesPython

About the role

Key responsibilities & impact

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.
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.

Requirements

What you’ll need

7+ 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.

Benefits

Comp & perks

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.