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Job Overview

Spearhead AI-driven Transformation in Investment Banking

We are seeking an exceptionally experienced and highly proficient Director of Applied AI Engineering to join our Investment Banking technology leadership team. This is a critical, C15-level role demanding deep technical expertise, a strategic mindset, and a strong client-facing orientation. The successful candidate will be a pioneer in integrating cutting-edge AI/ML solutions directly into client workflows across Equity Capital Markets (ECM), Debt Capital Markets (DCM), and Mergers & Acquisitions (M&A).

Operating with a high degree of autonomy, you will solve complex, high-impact problems, set technical direction, and champion an AI-first approach within a dynamic, client-centric environment. You will be instrumental in translating intricate business challenges in the financial domain into robust, scalable, and high-performance AI systems that deliver tangible value and competitive advantage to our clients and internal stakeholders. This role requires a techno-functional leader who can bridge the gap between advanced AI capabilities and critical business outcomes, fostering direct engagement with clients and business leads.

Key Responsibilities:

• Strategic AI/ML Solution Design & Implementation (Client-Focused): Lead the end-to-end design, architecture, and hands-on implementation of advanced AI/ML solutions and platforms directly supporting and enhancing Investment Banking client workflows in ECM, DCM, and M&A. This includes leveraging and adapting existing models, and pioneering new AI-driven approaches to meet specific client needs and strategic business objectives. Engage directly with clients to gather requirements, present solutions, and ensure successful adoption.
• Principal AI/ML Engineering & Technical Leadership: Act as a primary subject matter expert and thought leader in advanced AI/ML engineering, especially within the context of Investment Banking products. Provide overarching technical leadership, guidance, and mentorship to engineering teams and business stakeholders, fostering best practices in AI-augmented development, scalable system design, code reviews, and collaborative problem-solving. Champion a culture of quality through disciplined application of Spec-Driven Development (SDD), Test-Driven Development (TDD) / Behavior-Driven Development (BDD), and promote AI-driven test case generation and quality automation.
• Architect for Scalability, Resilience & Security: Provide deep expertise in modern application architecture, designing for cloud readiness by applying 12-Factor App principles and microservice patterns. Ensure AI solutions are built for optimal performance, scalability, resilience, and security within high-compliance environments (on-premise, hybrid cloud, or private cloud).
• Techno-Functional Partnership & Domain Expertise: Develop a deep understanding of Investment Banking products (ECM, DCM, M&A), collaborating closely with business stakeholders and external clients to identify critical business needs and high-impact AI opportunities. Act as a strategic partner, translating complex financial requirements into technical specifications and delivering AI solutions that directly address client pain points.
• Data Strategy & Engineering Leadership: Work closely with data engineers and data scientists to define advanced data requirements, ensure exceptional data quality, and optimize complex data pipelines for robust AI solution integration and deployment. Drive strategies for utilizing both structured financial datasets and unstructured data sources (filings, call transcripts, research) effectively.
• Model Deployment, Monitoring & Optimization: Oversee the deployment, scaling, monitoring, and continuous maintenance of AI/ML solutions in production environments. Implement advanced performance optimizations for AI solutions and underlying infrastructure to ensure efficient resource utilization, rapid inference, and proactive issue resolution.
• Strategic Vision, Innovation & Advocacy: Contribute significantly to the strategic vision for AI in Investment Banking by researching, evaluating, and advocating for new AI technologies, methodologies, and tools (e.g., LLMs, prompt engineering, Retrieval-Augmented Generation - RAG, MCP, A2A). Drive adoption of AI-powered tools (Devin, GitHub Copilot, Claude, Codex) to accelerate development, automate complex tasks, and validate architectural patterns across the SDLC. Embrace an agile, iterative mindset that avoids "Big Up-Front Design" (BUFD).
• Full-Stack Problem Solving & Database Mastery: Mastermind complex database interactions across Oracle, SQL, and MongoDB, employing AI to analyze query performance and recommend optimizations. Resolve high-impact problems across the entire stack through in-depth evaluation of business and system processes.

Qualifications & Experience:

• 10+ years of experience in software engineering, with at least 5+ years in a senior or lead Applied AI/ML engineering role specifically delivering complex, enterprise-grade, client-facing applications in financial services.
• Demonstrated success in building and deploying innovative AI applications within financial services, banking, or capital markets domains, with significant exposure to Investment Banking products (ECM, DCM, M&A).
• Proven experience leading technical implementations, mentoring other senior engineers, and directly engaging with clients in a principal capacity.
• Deep expertise in ML, NLP, LLMs, Retrieval-Augmented Generation (RAG), embeddings, and modern MLOps practices.
• Strong experience working with both structured financial datasets and unstructured data sources (e.g., SEC filings, call transcripts, research) within a regulated environment.
• Familiarity with front-office workflows in ECM, DCM, M&A, and investment research is essential.
• Exceptional communication and stakeholder management skills, with the ability to articulate complex technical and business concepts to diverse audiences, including senior executives and external clients.
• Advanced degree (Master's or Ph.D. preferred) in Computer Science, AI, Applied Mathematics, Engineering, or a related quantitative field.

Must-Have Engineering & Technical Acumen:

• Engineering Principles: Deep, practical knowledge of designing for cloud readiness, including microservice architecture, 12-Factor App principles, and modern design patterns.
• Development Methodologies: Proven experience championing and advocating for SDD (Spec Driven Development), TDD (Test Driven Development), and DDD (Domain Driven Design) within an agile environment.
• Core Backend Stack: Mastery of modern Java (JDK 17/21) and the core Spring Framework (Spring Boot, Spring MVC, Spring Data JPA, Spring Cloud).
• Frontend Technologies: Hands-on experience with modern UI frameworks like Angular, using TypeScript/JavaScript to build intuitive, client-facing user interfaces.
• Databases: Strong, hands-on experience with both relational (Oracle, SQL) and NoSQL (MongoDB) databases, with an emphasis on AI-driven optimization.
• DevOps & CI/CD: Proficiency with modern CI/CD pipelines (e.g., Harness, Jenkins), containerization with Docker, and container orchestration platforms (OpenShift, Kubernetes).

AI-First Expertise:

• Practical Application: Demonstrable, hands-on experience using and integrating AI development tools (e.g., Devin, GitHub Copilot, Claude, Codex) throughout the software development lifecycle for code generation, debugging, documentation, and automated quality assurance.
• Strategic Mindset: Strong understanding of the concepts underpinning modern AI tools (LLMs, prompt engineering, Retrieval-Augmented Generation - RAG, MCP, A2A) and a clear vision for leveraging them to transform engineering productivity, quality, and client value.

What Success Looks Like:

• Seamless integration of AI tools into daily workflows of Investment Banking clients and analysts, demonstrably enhancing their productivity and decision-making.
• Significant reduction in manual effort across critical client-facing activities such as targeting, pitch preparation, and market monitoring.
• AI-driven data assets and ML models fully aligned with enterprise governance, architecture, and client-specific requirements.
• A scalable AI platform that rapidly evolves to meet changing business needs and client demands, consistently delivering innovative solutions.

MUST HAVE: financial services, banking, or capital markets domains, with significant exposure to Investment Banking products (ECM, DCM, M&A).

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In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.

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