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.

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