Role Overview
Join a pioneering team at the forefront of financial technology innovation. We are building a cutting-edge, agentic AI platform designed to revolutionize the end-to-end credit risk model development lifecycle. By leveraging the power of Large Language Models (LLMs) and intelligent workflows, we aim to augment our quantitative modelers, dramatically increasing their productivity, enhancing model governance, and accelerating the delivery of critical risk models.
We are seeking a hands-on senior engineer to be a key contributor to this transformation. In this role, you will work closely with quantitative analysts and key stakeholders to understand and shape high-impact use cases and collaborate within a talented engineering team to design, build, and deploy the generative AI platform that brings this vision to life.
Key Responsibilities
- Take end-to-end ownership of discrete platform modules and drive them to completion, exercising sound judgement and initiative to navigate ambiguity and drive progress independently, with minimal supervision.
- Build proofs-of-concept and working prototypes to validate technical approaches early, iterating quickly based on stakeholder feedback in a fast-moving, discovery-driven environment.
- Design, develop, and ship production-grade Python components for the agentic AI platform — orchestration pipelines, RAG systems, LLM integrations, and MCP-based tool interfaces.
- Champion engineering best practices — clean code, automated testing, CI/CD, logging, and monitoring
- Build and maintain secure integrations with internal data sources, external APIs, and LLM providers in compliance with Citi's data governance and security standards
- Actively invest in self-learning to keep pace with the rapidly evolving GenAI and LLM landscape, bringing emerging tools and techniques to the team.
Qualifications: Graduate
Must-Haves:
- Extensive Software Engineering Experience: 8-12 years of hands-on experience designing, building, and shipping complex, scalable software systems to production.
- Expertise in Python: Deep proficiency in Python and its ecosystem of libraries for backend development and data processing.
- Generative AI Knowledge : An appetite to learn and apply GenAI concepts such as LLMs, RAG, and MCP, and agentic platforms such as Google ADK, with some evidence of prior exploration in these areas being a plus.
- Backend & Systems Fundamentals: Hands-on experience with REST APIs, SQL/databases, and Unix-based as well as containerized environments
- Technical Influence: Demonstrated ability to contribute to technical direction and champion best practices within an engineering team.
- Excellent Communication: Ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders and to translate business needs into concrete technical requirements.
- Global Collaboration: Demonstrated ability to work effectively with geographically distributed teams across multiple time zones.
Nice-to-Haves:
- Broader Language Proficiency: Experience with Java/C++ in addition to Python.
- Full-Stack Experience: Familiarity with front-end technologies and developing user interfaces for technical workflows.
- Financial Domain Knowledge: Experience in the financial services industry, particularly in the context of quantitative finance, risk management, or Model Risk Management (MRM).
- Familiarity with Model Development: Understanding of the market and/or credit risk model development lifecycle (e.g., for PD, LGD, EAD models) and associated regulatory requirements (e.g., SR 11-7, CCAR, CECL, etc.).
- Regulated Environments: Experience building auditable and compliant software within a highly regulated environment.
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Job Family Group:
Technology
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Job Family:
Applications Development
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Time Type:
Full time
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Most Relevant Skills
Please see the requirements listed above.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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