Lead AI and Machine Learning Engineer - Vice President OR Lead AI Engineer, Generative AI - Vice President

Job Description

We’re currently looking for a high caliber professional to join our team as Vice President, Application Development Tech Lead Analyst (C13) based in Pune, India.
 

XVA Technology at Citi is undertaking a bold, multi-year transformation to build a best-in-class centralized cross asset platform risk system. We are re-engineering our technology estate to achieve world-leading performance and resiliency, enabling new capabilities. Our ambition is to deliver a seamlessly integrated, highly automated platform that drives outstanding client outcomes and accelerates growth across our global franchise. As part of the team, you'll collaborate closely with high-caliber engineers and deeply engaged business and product partners - working together to define and deliver the next generation of XVA technology at Citi.

Role Overview/What will you do:

As an Applications Development Technology Lead Analyst, you will be instrumental in shaping the future of our product quality and delivery, working towards a target state of a fully automated platform with minimal manual QA intervention. This role specifically requires deep expertise in agentic AI and extensive hands-on experience in Generative AI (GenAI) projects. While a solid foundation in Machine Learning (ML) is beneficial, the successful candidate will be instrumental in architecting and implementing scalable AI frameworks, translating complex ideas into robust, production-ready systems, and contributing to Citi's strategic AI initiatives.
 

Key Responsibilities:

• GenAI Solution Development: Hands-on implementation, and deployment of scalable, robust agentic AI frameworks and Generative AI solutions for critical banking use cases, ensuring high performance, reliability, and security.
• Full-Stack Application Integration: Build full-stack applications that seamlessly integrate state-of-the-art Machine Learning (ML) and Large Language Model (LLM) tools and services into comprehensive AI solutions.
• AI Advancement & Prototyping: Proactively explore, prototype (Proof of Concept), and integrate the latest advancements in AI, particularly in agent-based systems, autonomous AI, and generative AI technologies.
• Collaboration: Fostering collaboration with cross-functional teams including AI researchers, data scientists, product managers, and software engineers to integrate and scale AI solutions across Citi’s products and services.
• Code Development & Standards: Write high-quality production code, drive proof-of-concepts, and validate architectural decisions through implementation. Set technical standards, guide design choices, and raise the bar for the team.
• Deployment: Take AI-enabled capabilities from early exploration and proof-of-concept through to secure, reliable, and production deployments, focusing on cloud-native, container-first services built to scale for enterprise use.
• Performance Evaluation: Design and implement rigorous metrics and evaluation strategies for AI system and agent performance, driving continuous optimization and behavioral improvement.
   

Qualifications:

• Experience: 12 to 15 years of deep hands-on experience in engineering and executing scalable enterprise solutions.
• AI Expertise: Deep expertise in AI principles, agent-based systems, machine learning, and advanced software engineering practices.
• Programming Proficiency: Expert proficiency in Python programming language is essential.
• GenAI Frameworks: Proven experience in implementing Generative AI use cases using frameworks like LangChain, AutoGen, CrewAI.
• LLM Implementation: Advanced knowledge in Large Language Models (LLM), with hands-on experience in implementing LLMs using vector databases and Retrieval-Augmented Generation (RAG), as well as tuning models. Ability to perform solution architecture validations for LLMs.
• ML & NLP: Understanding of machine learning models, experience with Natural Language Processing (NLP) libraries and tools.
• GenAIOps: Experience in putting Generative AI (GenAI) models into production and providing support to them (GenAIOps).
• MLOps: Experience using MLOps tools like MLflow for lifecycle management of machine learning models.
• Containerization: Experience in using Docker to create reproducible and scalable environments.
• Problem-Solving: Strong analytical and problem-solving skills with an aptitude for math and statistics.
• Communication: Excellent communication and collaboration skills, with the ability to engage effectively with diverse stakeholders.
• Self-Motivation: Self-motivated and capable of working independently as well as part of a team in a fast-paced environment.
• Business knowledge of CVA, XVA, regulatory stress testing is preferred

Education:

• Bachelor’s in Computer Science, Mathematics or equivalent.

• A Master’s degree in preferred

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