Discover your future at Citi

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Job Overview

This is a Senior Lead Principal Engineer position responsible for establishing and implementing innovative technology solutions and programs in coordination with the Technology Team. The overall objective of this role is to lead complex engineering initiatives across various domains, including Business Intelligence (BI) tools, Big Data, Agentic AI, and Machine Learning. We are looking for a Senior Principal Engineer with extensive experience building robust, high-performance, and large-scale applications. We are constantly evolving our significant software assets and are seeking a leader who is passionate about leveraging diverse technologies to drive technical excellence and innovation.

Team Background

The Credit Risk Technology Team is responsible for delivering Counterparty Credit Risk Management software solutions. These tools allow sales, traders, operations staff and risk officers to manage Citi's exposure to financial institutions, governments and corporates that trade with Citi. The team builds and maintains software that provides a complete platform for their users: limit monitoring, exception management, stress testing analysis and escalation workflow capabilities are some key areas of focus.

Key Responsibilities

• Provide technical leadership and development oversight to the wider team of Software Engineers/Analysts. Ensure application design adheres to the appropriate architectural and design patterns for scalable and intelligent systems.
• Develop key software components personally, and use your code as a teaching tool across the team for patterns and techniques, especially concerning Agentic AI design and implementation.
• Host sessions for design and code review at multiple levels of the organization. Improve our engineering standards and process to make the team more capable and efficient, embracing AI-assisted development practices, including the use of tools like Git Copilot and Devin AI.
• Collaborate effectively with a large global team of software engineers, business analysts, dev-ops and support staff to deliver software solutions for the business.
• Lead the adoption of new technologies where appropriate to solve business problems while adhering to Citi's architectural guidelines, with a strong focus on Agentic AI and Machine Learning frameworks.
• Serve as advisor or coach to mid-level/junior developers and analysts, helping build the overall engineering capabilities of the team, particularly in AI/ML and intelligent systems development.
• Appropriately assess risk when decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.

Ideal background

• Full-stack developer with 10+ years of experience in designing and developing robust, scalable, and maintainable applications applying Object Oriented Design principles.
• Hands-on experience in Web technologies (HTML, CSS, React JS), REST-APIs, and Backend using Java/J2EE technologies.
• Deep experience with Big Data technologies and data platforms capable of handling vast datasets for training, serving, and managing AI models.
• Strong knowledge of cloud platforms (AWS, Azure, or Google Cloud) and deployment/packaging solutions such as Dockers/Kubernetes, OpenShift.
• Experience with Agile software development processes with a strong emphasis on test driven development.
• Strong knowledge of CI/CD pipelines and experience in tools such as JIRA, BlackDuck, SONAR etc.
• Deep understanding of JVM internals such as class loading and memory management.

Skills

Extensive experience working with most if not all the technologies listed below:

• Java Core with extensive hands-on experience with concurrent programming.
• Spring Framework including Core, Integration, Batch, JDBC, Hibernate.
• Distributed Caching frameworks such as Oracle Coherence, Redis or equivalent.
• Cloud computing technologies with practical experience working with containers, microservices and large datasets (Docker, Kubernetes).
• Expertise in Big Data / Data Lake system architecture that supports enterprise data warehouse, real-time data processing, and large-scale data ingestion for AI/ML workloads.
• Databases - RDBMS preferably Oracle, NoSQL preferably MongoDB.
• Familiarity with Linux environment including scripting skills.
• SDLC/Dev Ops - Git/BitBucket, CI/CD pipeline frameworks such as Jenkins, SonarQube, JIRA, any secure coding toolkits.
• Demonstrated ability to integrate and optimize AI-assisted coding and development tools (e.g., Git Copilot, Devin AI) into team workflows to maximize efficiency and innovation.
• Hands-on experience with data governance, metadata management, and data lineage platforms to maintain high data quality and auditability for AI systems.
• Ability to evaluate design trade-offs and document choices effectively.
• Demonstrated capacity to build sophisticated tooling for development and production team use.

Preferred:

• Knowledge and experience with Business Intelligence (BI) tools such as Tableau and Cognos for data visualization and reporting.

• Experience with Big Data querying tools including Google BigQuery, Starburst, and DuckDB.
• Experience with prominent Machine Learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face) and their deployment in production.
• Deep understanding and practical application of Machine Learning algorithms (supervised, unsupervised, reinforcement learning) and statistical modeling, with a focus on interpretability and bias detection in financial applications.
• Knowledge of large language models, natural language processing (NLP), computer vision, and time-series analysis techniques as applied to Agentic AI and risk modeling.
• Experience in designing, developing, and implementing AI agents capable of autonomous decision-making, adaptive learning, and complex interaction within a financial domain.

Qualifications/Education

Graduate in a STEM (Science, Technology, Engineering and Mathematics) discipline. Master's degree in Data Science or AI/ML an advantage.

Competencies

• Excellent oral and written English.
• Strong leadership.
• Ability to collaborate in a large global team and influence key architectural decisions across groups.
• Ability to work well under pressure.

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