The Senior Vice President – Senior Lead, Python Engineering and Generative AI Platforms will lead the Retail and Wealth Risk Engineering organization within Enterprise Risk Technology. This senior leadership role focuses on defining strategy, architecture, and execution of enterprise-scale AI agent platforms and Python-based data ecosystems. The ideal candidate will manage a team of approximately 15 engineers, including AI-assisted developers, and drive the adoption of AI Product Development Lifecycle and regulatory-compliant data platforms. The role requires strong leadership and technical expertise to align technology initiatives with business priorities.

• 12+ years of relevant experience in enterprise application development, data engineering, or AI platform engineering.
• 8+ years of experience leading multi-team Agile organizations with 20+ engineers.
• Advanced expertise in Python, PySpark, and Databricks ecosystem for large-scale data processing.
• Proven experience architecting and implementing enterprise AI/GenAI platforms.
• Hands-on experience with AI-assisted development tools such as Devin.AI and GitHub Copilot.
• Strong experience with microservices architecture, APIs, and cloud-native deployment.
• Strong experience with event-driven architectures and streaming platforms.
• Deep understanding of data architecture, data mesh, and regulatory data requirements.
• Exceptional leadership, communication, stakeholder management, and decision-making capabilities.
• Experience with cloud platforms such as AWS, Azure, GCP, and Databricks.
• Familiarity with frontend technologies for full-stack solution delivery.
• Proven client relationship management experience.

• Lead multiple agile scrum teams comprising approximately 15 engineers, ensuring delivery excellence.
• Define and execute the enterprise strategy for Python engineering and AI agent platforms.
• Serve as the senior architect and technical authority for enterprise-scale AI agents and data engineering pipelines.
• Drive the adoption and operationalization of AI Product Development Lifecycle.
• Lead development of high-volume data pipelines and data federation layers.
• Architect and oversee GenAI agent ecosystems using LLMs.
• Drive AI-augmented software development lifecycle, integrating tools through advanced prompt engineering.
• Lead microservices and cloud-native architecture, ensuring high availability and performance.
• Drive engineering efficiency and standardization by reusing enterprise-level frameworks.
• Ensure all engineering solutions incorporate data governance and non-functional requirements.
• Act as a key partner to Risk, Finance, and Retail Banking stakeholders.
• Build and manage strong relationships with senior business leaders.
• Establish engineering standards, governance frameworks, and best practices across teams.
• Mentor senior engineers and engineering managers, fostering a culture of innovation.

• Citi offers a comprehensive benefits package including competitive pay, stock programs, healthcare coverage, retirement plans, paid time off, parental leave, and programs supporting employee wellbeing and professional development.