Lead Agentic AI Engineer - FDE ( Forward Deploy Engineer) at Citigroup

Job description

Job Description: Lead Agentic AI Engineer — Forward Deployed Engineer (FDE) 1.Role Overview & Mission As a Lead Agentic AI Engineer (Forward Deployed), you will operate at the intersection of applied GenAI engineering, autonomous multi-agent systems, and business domain problem-solving. Unlike traditional back-office research roles, a Forward Deployed Engineer (FDE) is embedded directly with business stakeholders, product leads, and operations teams to translate complex, unstructured institutional workflows into high-impact, enterprise-grade Autonomous Agent solutions. You will spearhead the design, prototyping, evaluation, and end-to-end production deployment of multi-agent architectures, agentic tool orchestration (e.g., Model Context Protocol / MCP, tool-calling pipelines), dynamic RAG retrieval engines, and human-in-the-loop autonomous decision workflows across institutional platforms (e.g., KYC Intelligence, Funds Lifecycle, Document Processing, and Regulatory Compliance). 2. Key Responsibilities Architecture & Agentic Engineering Responsibilities: AI Solutioning & Translation: Formulate and define systems scope and objectives for complex AI/GenAI projects, fostering seamless communication and translating business requirements into technical specifications for Data Scientists and ML Engineers. Implementation & Integration: Support the end-to-end AI implementation and system change processes—from translating Business Requirement Documents (BRDs) to functional specifications, through model deployment, testing, and integration with existing systems. Customization & Scripting: Consult with business clients to determine functional specifications for AI applications, developing custom scripts, integrations, or workflows to tailor AI/GenAI products to the unique needs of the business. Advanced Troubleshooting: Consult with users and clients to solve complex system and model integration issues through in-depth evaluation of business processes, AI/ML systems, and industry standards, recommending tailored technical solutions. Feedback Loop & Product Advocacy: Act as a critical bridge between front-line business users and core AI research/engineering teams. Gather direct feedback, identify model performance pain points, and relay insights from the field to help guide the AI product roadmap. Enablement & Training: Educate business and technical teams on how to leverage deployed AI solutions effectively, promoting best practices in prompt engineering, model interaction, and operational maintenance. Risk & Impact Management: Identify and communicate risks and impacts (including AI model limitations, data privacy, bias, and ethical considerations), evaluating the business implications of applying GenAI/ML technologies to the current business environment. Leadership & Collaboration: Act as advisor or coach to new or lower-level analysts and work as a team to achieve business objectives, performing other duties and functions as assigned. Autonomy: Has the ability to operate with a limited level of direct supervision, exercising independence of judgment and autonomy while acting as an SME to senior stakeholders and/or other team members. Compliance & Safeguarding: Appropriately assess risk when business 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. Forward Deployed Leadership & Execution High-Velocity Prototyping to Production: Rapidly convert ambiguous business problems into functional, production-ready Proof of Concepts (PoCs) in 2–4 week cycles, followed by hardened enterprise deployment. Client & Stakeholder Partnership: Sit alongside business heads, compliance officers, and operations leads to unc