AI Lead Engineer, Client Technology - SVP

Overview

We are seeking a highly skilled and pragmatic AI Lead to design, develop, and deploy advanced AI solutions. This hybrid position emphasizes technical leadership, rapid prototyping, iterative improvement, and delivering measurable business value by translating research ideas into robust, scalable production systems. The role focuses on creating intelligent AI agents capable of understanding goals, planning actions, and executing tasks with minimal human intervention, and on developing and implementing generative AI solutions.

• Agent and Generative AI Development: Design, implement, and deploy intelligent agents, including perception, reasoning, planning, and action execution modules. Contribute to the development and implementation of generative AI solutions, ensuring they meet technical requirements and business objectives.
• System Architecture & Scalability: Develop scalable and robust architectures for agentic systems and generative AI applications, ensuring high performance, reliability, and security.
• Machine Learning & LLM Integration: Integrate various machine learning models (e.g., LLMs, reinforcement learning, predictive models) to enhance agent capabilities and decision-making. Implement LLM integration using platforms like OpenAI, Anthropic, and Bedrock APIs.
• Task Automation & Workflow Optimization: Develop agents that can automate complex tasks, optimize workflows, and solve real-world problems across various domains.
• Rapid Delivery: MVP-first approach, iterative improvement with a focus on time-to-value, rapid iterations, hypothesis testing, and A/B experiments.
• Framework and Tooling: Utilize and contribute to agentic AI frameworks and development tools. Build full-stack applications that integrate existing ML/LLM tools and services.
• Evaluation and Optimization: Design and implement metrics and evaluation strategies for agent performance, continuously optimizing agent behavior.
• Research and Innovation: Stay abreast of the latest AI advancements in agent-based systems and generative AI. Contribute to proofs of concept and explore new methodologies.
• Collaboration & Leadership: Work with cross-functional teams to integrate agentic and generative AI solutions into products and services. Lead technical teams through coding and architectural decisions, promoting pragmatic buy-and-integrate approaches.
• Documentation: Create comprehensive technical documentation for agent designs, implementations, and operational procedures.

• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Robotics, or related quantitative field.
• Experience:
  • 10+ years software engineering experience with recent hands-on coding, track record of rapid delivery and launching multiple AI features in production.
  • Minimum 3+ years of professional experience in software development with a focus on AI, prompt engineering, machine learning, and/or agentic AI systems.
  • Experience in the finance industry is a plus.
• Programming Proficiency:
  • Strong proficiency in Python (FastAPI, Django, Flask, PySpark) or Java (Spring Boot, Spring Cloud, Spring Security), and SQL.
  • JavaScript (React, Next.js, Node.js, Type Script).
  • Full-stack development with a focus on rapid prototyping.
• AI/ML Expertise:
  • Solid understanding of core AI concepts, including knowledge representation, automated planning, decision-making under uncertainty, and multi-agent systems.
  • Experience with machine learning frameworks (e.g., Tensor Flow, PyTorch) and libraries (e.g., Scikit-Learn, Num Py, Pandas).
  • Familiarity with large language models (LLMs) and their application in agentic systems.
  • Familiarity with agent frameworks (e.g., Lang Chain, Auto Gen, CrewAI, RAG) or research in multi-agent reinforcement learning.
  • Experience in designing and implementing APIs for AI services.
• Software Engineering: Experience with software development best practices, including version control, CI/CD, testing, and code reviews. Understanding of agile methodologies, resiliency, and security in AI projects. Proven experience in system design and operational stability.
• Application and Data Architecture: Experience with data flows, data engineering practices to support AI model training and deployment. API-first design emphasizing microservices and event-driven architectures.
• Containerization: Experience with Docker and Kubernetes.
• Problem-Solving: Excellent analytical and creative problem-solving skills.
• Communication: Strong written and verbal communication skills to articulate complex technical concepts.

Equal Opportunity:
Citi is an equal opportunity employer and complies with all applicable EEO laws. Qualified candidates will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.