Senior Python Engineer — AI-Driven Risk Model Automation

Citigroup Inc.; New York (NY); On-site; USD 142,000 - 213,000; Full time

Job summary

Citigroup Inc. in New York seeks a Senior Python Developer in Risk Technology to design and deliver AI-enabled automation across end-to-end model lifecycles for market and credit risk models.

This hands-on engineering role builds tooling for AI-assisted documentation, automated testing, data analysis, and governance, collaborating with quants, validators, and data engineers to translate requirements into robust production-grade solutions.

Qualifications

• 7+ years of professional software development experience with Python and its data/engineering ecosystem.
• Proven track record of delivering production-grade automation, data pipelines, or testing frameworks in a complex enterprise environment.
• Solid AI/ML knowledge with practical experience and/or LLM-based application development.
• Strong experience with test automation, CI/CD, and modern software engineering practices.
• Experience working with large datasets, data quality checks, and SQL in enterprise data platforms.
• Ability to communicate technical concepts to non-technical stakeholders in a cross-functional team.

Responsibilities

• Design, develop, and maintain Python-based services, pipelines, and tools for automation of the model lifecycle across risk model families.
• Build and operate automated testing/validation frameworks for quantitative models, including benchmarking and reporting.
• Develop data analysis and reconciliation tooling over large risk datasets with data lineage and traceability.
• Contribute to model lifecycle tooling: inventory, workflow orchestration, approvals, and audit-ready evidence.

Skills

Python

Pandas

NumPy

FastAPI

Data engineering

CI/CD

Test automation

SQL

LLM / AI/ML

Git

Education

STEM degree

Tools

Docker

Kubernetes

Airflow

Git

Job description

Citigroup Inc. in New York seeks a Senior Python Developer in Risk Technology to design and deliver AI-enabled automation across end-to-end model lifecycles for market and credit risk models.

This hands-on engineering role builds tooling for AI-assisted documentation, automated testing, data analysis, and governance, collaborating with quants, validators, and data engineers to translate requirements into robust production-grade solutions.