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

Citi

. Architect complex, multi-agent AI workflows capable of advanced reasoning, planning, and autonomous execution using LangGraph, CrewAI, and Google ADK .

Posted 11/11/2026full-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, MLOps principles, and scalable backend services. Proficient in deploying containerized AI systems on cloud platforms while ensuring compliance with AI risk and governance standards.

Highest-signal resume keywords

Expert-Level Python DevelopmentMLOps ImplementationContainerized AI Systems DeploymentAgentic Frameworks ExperienceRAG Systems Architecture

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Hard Skills

Python DevelopmentFastAPILangChainLangGraphCrewAIAutoGenDockerKubernetesRAG SystemsMachine Learning

Tools & Technologies

Google ADKLangfuseAzure AI SearchPineconeAWS BedrockGCP Vertex AICI/CD PipelinesObservability FrameworksVector DatabasesDocument Ingestion

Certifications & Qualifications

AWS Certifications

Industry Keywords

Financial ServicesRegulated IndustryAI RiskSafetyEnterprise Governance

Tech Stack

Tools & technologies

AWSAzureDockerGoogle Cloud PlatformKubernetesMicroservicesPython

About the role

Key responsibilities & impact

Architect complex, multi-agent AI workflows capable of advanced reasoning, planning, and autonomous execution using LangGraph, CrewAI, and Google ADK
Translate complex risk-domain business problems into technical requirements and develop robust end-to-end AI solutions
Implement agentic AI workflows using LangGraph, CrewAI, and AutoGen, focusing on reasoning, tool use, and memory
Build and optimize retrieval pipelines, memory layers, and tool-use sequences using LangChain
Develop scalable Python-based microservices and REST APIs using FastAPI
Construct and refine RAG pipelines, including document ingestion, embedding, and vector search integration with Azure AI Search or Pinecone
Package AI services with Docker and deploy them on Kubernetes, contributing to CI/CD pipelines
Instrument AI workflows with Langfuse for tracing and debugging
Implement and maintain evaluation harnesses to ensure model quality and performance

Requirements

What you’ll need

6+ years of professional experience in a role blending software development and data science/machine learning
Expert-level Python development skills
Proven track record designing and building scalable backend services and APIs; FastAPI preferred
Deep, hands-on experience with multiple agentic frameworks, including LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel
Extensive experience architecting and optimizing RAG systems and integrating vector databases such as OpenSearch, Pinecone, or Weaviate
Expertise designing and deploying containerized Docker/Kubernetes AI systems on 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, NLP, and Transformer architectures
Experience leading LLM evaluation harnesses for automated release validation
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 or equivalent preferred
Background in financial services or another highly regulated industry preferred

Benefits

Comp & perks

Equal opportunity employment
Reasonable accommodation for persons with disabilities