Lead AI & Machine Learning Engineer ,Vice President

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

The XVA technology team actively develops and enhances a set of co-operative, strategic systems and services which are regulatory driven and have a direct bearing on how Citi Capital Markets manages risk arising from trading, including both market and counterparty credit risks. We support traders, salespeople, risk managers, financial controllers and operations staff. The Application Development Tech Lead Analyst will be responsible for participation in the establishment and implementation of new or revised application systems and programs in coordination with the Technology team. 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+ years of deep hands-on experience in engineering and executing scalable enterprise solutions.
• Education: Bachelor's degree in Computer Science, Computer Engineering, or a related technical field is required.
• 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.

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