Lead AI Engineer - Vice President

• Architectural Leadership: Design and architect end-to-end generative AI solutions, from proof-of-concept to production, ensuring scalability, performance, and reliability.
• Technical Strategy: Develop and maintain a comprehensive strategic roadmap for generative AI adoption, evaluating new models, techniques, and platforms to keep our capabilities at the forefront of the industry.
• Solution Development: Lead the hands-on development of complex AI systems, including Retrieval-Augmented Generation (RAG) pipelines, autonomous AI agents, fine-tuning workflows, and custom model integrations.
• Best Practices & Standards: Establish and govern best practices for the full AI development lifecycle, including prompt engineering, model evaluation, MLOps, and data management.
• Cross-Functional Partnership: Collaborate closely with multiple management teams and business units to identify high-impact use cases and ensure the successful integration of AI solutions to meet business goals.
• Mentorship & Guidance: Serve as a senior advisor and coach to other engineers and analysts, fostering a culture of innovation and technical excellence. Allocate work and provide technical direction to the team.
• Risk & Compliance: Appropriately assess risk when business decisions are made, demonstrating consideration for the firm's reputation and safeguarding its clients and assets. Drive compliance with all applicable laws, rules, and regulations, particularly those related to AI ethics, data privacy, and model bias.
• Innovation and Research: Stay abreast of the latest advancements in generative AI research, and translate state-of-the-art developments into practical, innovative solutions.

• Experience: Extensive experience in designing and building AI/ML solutions, with a significant focus on generative AI and Large Language Models (LLMs).
• Gen AI Expertise: Deep understanding of modern AI architectures and techniques, including Retrieval-Augmented Generation (RAG), fine-tuning, function calling, and AI agentic workflows.
• Programming Proficiency: Expert-level skills in Python and extensive experience with core AI/ML libraries such as PyTorch, TensorFlow.
• System Design: Proven ability to architect and develop large-scale, distributed, multi-tier applications. Strong knowledge of microservices, API design, and system integration.
• MLOps: Solid understanding of MLOps principles and experience with tools for model versioning, deployment, monitoring, and lifecycle management.
• Leadership: Demonstrated experience serving as a technical lead, architect, or principal engineer, with a track record of mentoring team members and driving projects to completion.

• Vector Databases: Experience with vector databases and search technologies (e.g., Pinecone, Milvus, ChromaDB, Elasticsearch).
• Data Engineering: Familiarity with data pipeline development (ETL/ELT) and big data frameworks like Apache Spark or Kafka.
• Containerization: Experience with containerization and orchestration technologies such as Docker and Kubernetes for deploying and scaling AI services.
• Performance Optimization: Knowledge of distributed caching solutions (e.g., Redis, Hazelcast) and building highly performant, low-latency systems.

Citigroup