Vice president lead AI ML engineer

The Specialized Analytics VP is a developing role for an AI/ML professional. This role is focused on designing and building cutting-edge solutions by harnessing the power of both advanced machine learning and Large Language Models. The ideal candidate will have a deep understanding of the modern AI landscape, with hands-on experience applying these technologies to solve real-world business challenges. You will integrate in-depth specialty area knowledge with a solid understanding of industry standards and practices, and will have the latitude to solve complex problems independently. This role has a direct impact on the business's core activities, and the quality of your work will influence the success of your team and its partners. The role will report to the VP leading the team. Responsibilities: • Design, develop, and deploy end-to-end solutions leveraging Large Language Models (LLMs), with a strong focus on building and optimizing Retrieval-Augmented Generation (RAG) systems. • Apply the fundamentals of Agentic AI to create autonomous and intelligent systems that solve complex business problems. • Utilize a broad range of machine learning techniques, including predictive modeling (e.g., Gradient Boosting, XGBoost) and unsupervised learning (e.g., Clustering), to generate insights and enhance AI-driven solutions. • Lead advanced prompt engineering efforts and establish robust testing frameworks to ensure the performance, accuracy, and safety of LLM-based applications. • Work with large and complex datasets to source, clean, and prepare data for both traditional modeling and advanced LLM applications. • Translate complex AI/ML concepts and analytical findings into actionable insights and communicate them clearly to both technical and non-technical stakeholders. • 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. Qualifications: •10+ years of relevant hands-on experience in an AI, ML, or data science role. • Strong foundation in classical machine learning algorithms (e.g., Gradient Boosting, Clustering, XGBoost) and their practical applications. • Deep understanding of Large Language Models (LLMs), and proven, hands-on experience building end-to-end Retrieval-Augmented Generation (RAG) pipelines. • Demonstrable expertise in advanced prompt engineering techniques and a strong grasp of LLM evaluation and testing methodologies. • Strong proficiency in Python and its core AI/ML libraries • Expertise in retrieving and manipulating large datasets using SQL. • Excellent communication and interpersonal skills, with the ability to build partnerships with cross-functional leaders in a matrix work environment. • A proactive, can-do attitude and a strong willingness to learn and adapt within the rapidly evolving AI landscape. Education: • Bachelors/University degree or equivalent experience in a quantitative field such as Computer Science, Statistics, Engineering, or Mathematics.

The North America Consumer Bank – Data Science and Modeling team analyzes millions of prospects and billions of customer level transactions using big data tools and machine learning, AI techniques to unlock opportunities for our clients in meeting their financial needs and create economic value for the bank. The team extracts relevant insights, identifies business opportunities, converts business problems into modeling framework, uses big data tools, latest deep learning and machine learning algorithms to build predictive models, implements solutions and designs go-to-market strategies for a huge variety of business problems. The Specialized Analytics VP is a developing role for an AI/ML professional. This role is focused on designing and building cutting-edge solutions by harnessing the power of both advanced machine learning and Large Language Models. The ideal candidate will have a deep understanding of the modern AI landscape, with hands-on experience applying these technologies to solve real-world business challenges. You will integrate in-depth specialty area knowledge with a solid understanding of industry standards and practices, and will have the latitude to solve complex problems independently. This role has a direct impact on the business's core activities, and the quality of your work will influence the success of your team and its partners. The role will report to the VP leading the team. Responsibilities: • Design, develop, and deploy end-to-end solutions leveraging Large Language Models (LLMs), with a strong focus on building and optimizing Retrieval-Augmented Generation (RAG) systems. • Apply the fundamentals of Agentic AI to create autonomous and intelligent systems that solve complex business problems. • Utilize a broad range of machine learning techniques, including predictive modeling (e.g., Gradient Boosting, XGBoost) and unsupervised learning (e.g., Clustering), to generate insights and enhance AI-driven solutions. • Lead advanced prompt engineering efforts and establish robust testing frameworks to ensure the performance, accuracy, and safety of LLM-based applications. • Work with large and complex datasets to source, clean, and prepare data for both traditional modeling and advanced LLM applications. • Translate complex AI/ML concepts and analytical findings into actionable insights and communicate them clearly to both technical and non-technical stakeholders. • 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. Qualifications: •10+ years of relevant hands-on experience in an AI, ML, or data science role. • Strong foundation in classical machine learning algorithms (e.g., Gradient Boosting, Clustering, XGBoost) and their practical applications. • Deep understanding of Large Language Models (LLMs), and proven, hands-on experience building end-to-end Retrieval-Augmented Generation (RAG) pipelines. • Demonstrable expertise in advanced prompt engineering techniques and a strong grasp of LLM evaluation and testing methodologies. • Strong proficiency in Python and its core AI/ML libraries • Expertise in retrieving and manipulating large datasets using SQL. • Excellent communication and interpersonal skills, with the ability to build partnerships with cross-functional leaders in a matrix work environment. • A proactive, can-do attitude and a strong willingness to learn and adapt within the rapidly evolving AI landscape.