AI Lead Engineer, Client Technology - SVP

Citi•New York, NY

About The Position

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 approach with a focus on "time to value" (quick iterations, hypothesis testing, 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 and improving agent behavior. Research and Innovation: Stay abreast of the latest advancements in AI, particularly in agent-based systems, autonomous AI, and related fields, and propose innovative solutions. Demonstrate deep expertise in generative AI technologies, actively participating in the development of proofs of concept (POCs) and exploring new methodologies. Collaboration & Leadership: Work closely with cross-functional teams (AI researchers, data scientists, product managers, software engineers) to integrate agentic and generative AI solutions into broader products and services. Lead technical teams through hands-on coding and architectural decisions, championing pragmatic "buy and integrate" approaches. Documentation: Create comprehensive technical documentation for agent designs, implementations, and operational procedures.

Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field.
10+ years software engineering experience with recent hands-on coding, with a 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.
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., TensorFlow, PyTorch) and relevant libraries (e.g., Scikit-Learn, NumPy, Pandas).
Familiarity with large language models (LLMs) like ChatGPT, LaMDA/Gemini, Llama, etc., and their application in agentic systems.
Familiarity with specific agent frameworks (e.g., LangChain, AutoGen, CrewAI, RAG) or research in multi-agent reinforcement learning.
Experience in designing and implementing APIs for AI services.
Experience with software development best practices, including version control (Git), CI/CD pipelines, testing, and code reviews.
Understanding of agile methodologies, application resiliency, and security applied to AI projects.
Proven experience in system design, application development, and operational stability in AI projects.
Experience with application and data architecture patterns and designs.
Thorough understanding of data flows from producer to consumer systems.
Familiarity with data engineering practices to support AI model training and deployment.
Leveraging managed services and existing platforms, with an API-First Design emphasizing microservices and event-driven architectures.
Experience with Docker and Kubernetes.
Excellent analytical and problem-solving skills with a creative approach to complex challenges.
Strong written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.

Nice To Haves

Experience in the finance industry is a plus.

Responsibilities

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.
Develop scalable and robust architectures for agentic systems and generative AI applications, ensuring high performance, reliability, and security.
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.
Develop agents that can automate complex tasks, optimize workflows, and solve real-world problems across various domains.
Utilize and contribute to agentic AI frameworks and development tools.
Build full-stack applications that integrate existing ML/LLM tools and services.
Design and implement metrics and evaluation strategies for agent performance, continuously optimizing and improving agent behavior.
Stay abreast of the latest advancements in AI, particularly in agent-based systems, autonomous AI, and related fields, and propose innovative solutions.
Demonstrate deep expertise in generative AI technologies, actively participating in the development of proofs of concept (POCs) and exploring new methodologies.
Work closely with cross-functional teams (AI researchers, data scientists, product managers, software engineers) to integrate agentic and generative AI solutions into broader products and services.
Lead technical teams through hands-on coding and architectural decisions, championing pragmatic "buy and integrate" approaches.
Create comprehensive technical documentation for agent designs, implementations, and operational procedures.