Senior Python Developer - Quant Models AI Automation, Vice President

Citi; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Citi in the United Kingdom is seeking a senior Python Developer to join Risk Technology on a multi-year initiative designing AI-enabled automation across the end-to-end model lifecycle for market and credit risk models.

You will build tools and services for AI-assisted documentation, automated testing, data analysis, and lifecycle management, collaborating with quants, validators, data engineers, and program leadership to deliver production-grade solutions.

Qualifications

• Degree in STEM fields; Master’s degree preferred.
• Extensive Python development experience in data/engineering.
• Proven delivery of production-grade automation in finance.
• Strong AI/ML knowledge and LLM-based workflows.
• Experience with test automation, CI/CD, and version control.
• Experience with large data sets and SQL.

Responsibilities

• Design, develop, and maintain Python-based services, pipelines, and tools for automating the model lifecycle.
• Build automated testing and validation frameworks for quantitative models.
• Develop data analysis and reconciliation tooling over large risk datasets.
• Contribute to model lifecycle tooling including inventory, orchestration, reviews, and audit-ready evidence.
• Implement AI components within the workflow and ensure auditable production environment.

Skills

Python

Pandas

NumPy

FastAPI

CI/CD

SQL

Data analysis

Cloud AWS

Education

STEM degree

Master's degree preferred

Tools

Docker

Kubernetes

Airflow

Git

Job description

We are seeking a senior Python Developer within Risk Technology to join a multi-year strategic initiative: the design and delivery of AI-enabled automation across the end-to-end quantitative model lifecycle, covering all market risk and credit risk models.

This is a hands‑on engineering role at the core of the program. You will design and build the tooling and services that power AI‑assisted model documentation, automated model testing and validation workflows, large‑scale risk data analysis, and model lifecycle management. You will work closely with quantitative analysts, model validators, data engineers, and the program leadership to translate workflow automation requirements into robust, production‑grade solutions.

Key Responsibilities

Engineering & Delivery

• Design, develop, and maintain Python‑based services, pipelines, and tools supporting automation of the quantitative model lifecycle across market risk and credit risk model families.
• Build and operate automated testing and validation frameworks for quantitative models, including test orchestration, result capture, benchmarking, and reporting.
• Develop data analysis and reconciliation tooling over large‑scale risk datasets, ensuring data quality, lineage, and traceability across risk platforms.
• Contribute to model lifecycle management tooling: model inventory, workflow orchestration, approvals, periodic reviews, and audit‑ready evidence generation.

AI Enablement

• Implement AI/ML components within the model workflow, including LLM‑based automation for model documentation generation and updating, intelligent data quality checks, and workflow assistance.
• Integrate AI tooling into a controlled, auditable, production‑grade environment, with appropriate testing, monitoring, and governance controls.
• Stay current with developments in applied AI/ML and evaluate their applicability to risk technology use cases.

Collaboration & Standards

• Work within a cross‑functional agile team alongside quants, validators, data engineers, and program management.
• Promote engineering best practices: code quality, testing, CI/CD, documentation, and secure, scalable design.
• Mentor junior developers and contribute to technical design reviews.

Required Qualifications

• STEM degree (Computer Science, Engineering, Mathematics, Statistics, Physics, or related); Master's degree preferred.
• Professional software development experience, with deep expertise in Python and its data/engineering ecosystem (e.g., pandas, NumPy, FastAPI, orchestration tools).
• Proven track record of delivering production‑grade automation, data pipelines, or testing frameworks in a complex enterprise environment; financial services experience strongly preferred.
• Solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM‑based application development (e.g., API integration, prompt engineering, RAG‑style document workflows).
• Strong experience with test automation, CI/CD, and modern software engineering practices (Git, code review, containerization).
• Experience working with large datasets, data quality/linearity checks, and SQL; familiarity with enterprise data platforms.
• Ability to work effectively in a cross‑functional, global team and communicate technical concepts to non‑technical stakeholders.

Preferred Qualifications

• Exposure to quantitative risk models (market risk and/or credit risk) and the model lifecycle: development, validation, documentation, and ongoing monitoring.
• Familiarity with the model risk regulatory landscape and governance expectations in banking.
• Experience with workflow orchestration platforms, cloud environments (AWS/Google Cloud), and container orchestration (Docker/Kubernetes).
• Background in quantitative finance, statistics, or data science; an advanced quantitative degree is a plus.
• Experience mentoring engineers and leading small technical workstreams.

Job Family Group:

Technology

Job Family:

Applications Development

Time Type:

Full time

Most Relevant Skills

Please see the requirements listed above.

Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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