Risk Modeling Solutions - Full-stack GenAI - Analyst II

Citi

Full-stack GenAI Engineer in Risk Modeling Solutions developing AI workflows and applications for risk management. Responsibilities include API development, AI orchestration, and engineering excellence.

Posted 7/16/2026full-timeBangalore • 🇮🇳 IndiaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing AI workflows, microservices, and APIs, with a strong focus on Python, containerization, and retrieval-augmented generation techniques. Proficient in utilizing frameworks and tools for building scalable AI systems and ensuring high engineering standards.

Highest-signal resume keywords

Python DevelopmentFastAPI API DevelopmentDocker ContainerizationKubernetes DeploymentAgentic AI Frameworks

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Applicant Tracking System Keywords

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Hard Skills

PythonMicroservices ArchitectureREST APIsRetrieval-Augmented GenerationVector DatabasesLangChainLangGraphCrewAIRAG ArchitectureNLP Fundamentals

Soft Skills

Collaborative DevelopmentTechnical Documentation

Tools & Technologies

DockerKubernetesLangfuseOpenSearchPineconeChroma

Industry Keywords

AI WorkflowsMachine LearningData ScienceTransformers

Tech Stack

Tools & technologies

DockerKubernetesMicroservicesPython

About the role

Key responsibilities & impact

Implement end-to-end agentic AI workflows using frameworks like LangGraph, CrewAI, and AutoGen.
Build and optimize retrieval pipelines, memory layers, and tool-use sequences using frameworks like LangChain.
Develop robust, scalable Python-based microservices and REST APIs using FastAPI.
Construct and refine Retrieval-Augmented Generation (RAG) pipelines.
Package AI services using Docker and deploy them on Kubernetes.
Instrument AI workflows using platforms like Langfuse for tracing and debugging.
Contribute to technical documentation and uphold high engineering standards.

Requirements

What you’ll need

2+ 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).
Practical experience with RAG architecture, including vector databases (e.g., OpenSearch, Pinecone, Chroma).
Familiarity with Git-based workflows and collaborative development practices.
A solid understanding of NLP fundamentals and Transformer architectures.

Benefits

Comp & perks

Equal opportunity employer
Accommodation for disabled individuals