Intermediate Analyst, Risk Modeling Solutions – Full-stack GenAI

Citi

Full-Stack Gen AI Engineer developing AI workflows and microservices for risk management solutions. Collaborating on end-to-end AI architectures and robust backend systems using Python and FastAPI.

Posted 7/16/2026full-timeBangalore • 🇮🇳 IndiaMid-LevelSeniorWebsite

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 implementing scalable AI architectures and collaborating in high-standard engineering environments.

Highest-signal resume keywords

Python Development ExpertiseFastAPI Microservices ArchitectureDocker and Kubernetes ProficiencyAgentic AI Frameworks ExperienceRAG Architecture Implementation

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Applicant Tracking System Keywords

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Hard Skills

PythonFastAPIDockerKubernetesLangChainLangGraphCrewAIRAG ArchitectureVector DatabasesNLP Fundamentals

Soft Skills

CollaborationCode ReviewTechnical Documentation

Tools & Technologies

LangfuseAzure AI SearchPineconeOpenSearchChromaAWS BedrockAzure AI FoundryGCP Vertex AISnowflakeRedshift

Industry Keywords

Financial ServicesRegulated Enterprise Environment

Tech Stack

Tools & technologies

Amazon RedshiftAWSAzureCloudDockerGoogle Cloud PlatformKubernetesMicroservicesPython

About the role

Key responsibilities & impact

Implement end-to-end agentic AI workflows using frameworks like LangGraph, CrewAI, and AutoGen, focusing on reasoning, tool use, and memory.
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 to expose AI capabilities.
Construct and refine Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding, and vector search integration with databases like Azure AI Search or Pinecone.
Package AI services using Docker and deploy them on Kubernetes, contributing to CI/CD pipelines for smooth and reliable releases.
Instrument AI workflows using platforms like Langfuse for tracing and debugging.
Implement and maintain evaluation harnesses to ensure model quality and performance.
Actively participate in code reviews, contribute to technical documentation, and collaborate with team members to uphold high engineering standards.

Requirements

What you’ll need

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).
Practical experience with RAG architecture, including vector databases (e.g., OpenSearch, Pinecone, Chroma).
Proven ability to design and implement cost-effective AI architectures, with a deep understanding of tokenomics, model-tiering strategies, and caching for performance and budget management.
Familiarity with Git-based workflows and collaborative development practices.
A solid understanding of NLP fundamentals and Transformer architectures.
Preferred Qualifications Experience with Langfuse or similar AI observability platforms.
Hands-on use of a major cloud AI platform (AWS Bedrock, Azure AI Foundry, or GCP Vertex AI).
Familiarity with enterprise data platforms like Snowflake or Redshift.
Background in financial services or another regulated enterprise environment.

Benefits

Comp & perks

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