We are seeking an exceptional and highly motivated GenAI Tech Lead to join our dynamic team and drive the design, prototyping, and implementation of advanced Generative AI solutions within Finance Technology. This pivotal role requires a seasoned professional with a deep understanding of Generative AI technologies, a strong hands-on approach, and a proven ability to translate complex business challenges into robust, scalable, and impactful digital solutions, particularly focusing on GenAI adoption and Agentic AI for automating business processes within the financial sector. If you are passionate about innovation, technical leadership, and influencing strategic direction, we encourage you to apply!
Key Responsibilities:
- Experience Range 11 Years Plus to 15 Years
- Strategic Leadership & GenAI Adoption: Drive the identification, evaluation, and adoption of emerging Generative AI technologies and tools to develop innovative solutions and enhance existing platforms. Actively design, develop, and implement AI/ML solutions within financial applications, with a strong emphasis on practical GenAI adoption. This includes hands-on coding, prototyping, and deploying GenAI solutions.
- Agentic AI for Business Process Automation: Lead the end-to-end design, prototyping, and implementation of Agentic AI solutions focused on automating critical business processes, ensuring they address business needs and scale effectively across the enterprise. This includes designing and implementing Retrieval Augmented Generation (RAG) systems to enhance accuracy and contextuality, especially for financial data.
- Chatbot Development: Design, develop, and deploy intelligent chatbot solutions capable of interacting with users across both structured data sources (e.g., databases, APIs) and unstructured data (e.g., documents, emails, free text), leveraging GenAI for natural language understanding and generation in complex conversational flows.
- Document Processing & OCR Integration: Implement solutions for processing and extracting information from documents, including integrating Optical Character Recognition (OCR) technologies to handle scanned documents and images, making their content available for GenAI models.
- Text-to-SQL Implementation: Develop and deploy solutions leveraging Large Language Models to facilitate Text-to-SQL capabilities, enabling users to generate SQL queries from natural language requests, thus simplifying data access and analysis from structured databases.
- Data Analysis & Feature Engineering: Analyze large, complex datasets, identify intricate patterns, and extract actionable insights, leveraging GenAI capabilities for data augmentation or synthetic data generation, particularly relevant for Agentic AI applications.
- Data Pipeline Development: Build and maintain robust, scalable data pipelines for data ingestion, processing, and transformation, specifically optimizing for the unique requirements of GenAI model training and inference.
- Cross-Functional Collaboration: Partner with multiple management teams and business stakeholders to deeply understand requirements and translate them into precise technical specifications and actionable roadmaps, incorporating GenAI and Agentic AI possibilities into discussions.
- System Enhancements & Architecture: Identify and define necessary system enhancements to deploy new GenAI products and process improvements. Ensure application design adheres to the overall architecture blueprint, integrating GenAI and Agentic AI components seamlessly.
- Problem Resolution: Resolve a variety of high-impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards, applying GenAI and Agentic AI where they can offer innovative solutions.
- Tool & Technology Evaluation: Evaluate and select appropriate Generative AI tools and technologies, including specialized frameworks, libraries, and cloud AI services (e.g., AWS SageMaker, Azure ML, Google AI Platform) with a focus on GenAI adoption and Agentic AI capabilities.
- Documentation & Monitoring: Develop and maintain comprehensive documentation for AI/ML models and systems, specifically addressing GenAI and Agentic AI implementations. Manage and monitor the performance, efficiency, and reliability of deployed AI/ML models, including evaluating the effectiveness of GenAI components in production.
- Technical Mentorship & Leadership: Provide guidance and mentorship to junior engineers and analysts, fostering best practices in GenAI and Agentic AI development, deployment, and operational excellence, and allocating work as necessary.
- Risk Assessment: 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, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency, especially concerning responsible AI and GenAI ethics in the context of GenAI adoption and Agentic AI.
- Agile Development & Proof-of-Concept: Champion rapid delivery and iterative development, prioritizing value delivery over upfront perfection. Lead the development of compelling proof-of-concept projects to validate the feasibility and potential of novel GenAI and Agentic AI solutions.
- Platform Contribution: Actively contribute to the design and development of internal GenAI and Agentic AI platforms, frameworks, and shared services.
- Troubleshooting Support: Provide expert technical support, troubleshooting, and resolution for GenAI and Agentic AI solutions in production environments.
Required Skills & Technical Expertise:
- Experience: 10-15 years of relevant experience in Apps Development or systems analysis role, with a significant portion dedicated to AI/ML and demonstrable hands-on experience in designing, building, and deploying real-world Generative AI solutions, with a specific focus on GenAI adoption and Agentic AI for business process automation.
- Programming Fluency: Proficiency in at least two programming languages. Strong preference for Python, with significant experience in Javascript/Typescript and Golang being highly valued.
- Scalable Solutions: Demonstrated deep hands-on experience in engineering and deploying enterprise-grade solutions that are highly scalable, resilient, and performant.
- Generative AI Expertise:
- Strong theoretical and practical understanding of Large Language Models (LLMs), including experience with various open-source and proprietary models, fine-tuning techniques, and deployment strategies.
- Proficiency with transformers, agentic frameworks (critical for business process automation), vector stores (e.g., Pinecone, FAISS, ChromaDB), and advanced search algorithms.
- Hands-on experience with Retrieval Augmented Generation (RAG) implementations.
- Experience with relevant GenAI/ML frameworks such as LangChain, LangGraph, MLFlow, Spring AI, Spring Boot, and Flask.
- Data & Database Proficiency:
- Extensive experience with data analysis and manipulation using tools like SQL and Pandas.
- Proficiency in database technologies including Oracle, Postgres, or MongoDB.
- API & Integration:
- Proven experience in designing and implementing robust REST and WebSocket APIs.
- Experience with messaging and integration platforms like Kafka or JMS/MQ.
- Frontend Development: UI development skills with technologies such as React JS or Streamlit.
- Production AI/ML/GenAI: Demonstrated ability to design, develop, and deploy GenAI and Agentic AI solutions into production environments.
- MLOps: Experience with MLOps principles and tools is a significant advantage.
- Technical Proficiency: Extensive experience in system analysis and in programming of software applications, with deep expertise in Python for AI/ML development.
- Communication: Consistently demonstrates clear and concise written and verbal communication, capable of articulating complex technical concepts to both technical and non-technical stakeholders.
- Project Management: Experience in managing and implementing successful projects; demonstrated leadership and project management skills within an agile development framework.
- Subject Matter Expertise: Subject Matter Expert (SME) in at least one area of Applications Development or AI/ML domain, with a strong understanding of financial services concepts preferred.
- Adaptability: Ability to adjust priorities quickly as circumstances dictate in a fast-paced environment.
Qualifications:
- Education: Bachelor’s degree/University degree in Computer Science, Engineering, Data Science, or a related quantitative field, or equivalent experience. Master’s degree or Ph.D. preferred, especially with a focus on Artificial Intelligence, Machine Learning, or Natural Language Processing.
- Financial Services/Technology Background: 10+ years of professional experience, preferably within financial services or leading technology firms.
- Innovation & Problem Solving: A self-starter with a strong drive for innovation, exhibiting exceptional problem-solving skills, and a knack for leveraging "out-of-the-box" tooling and automation.
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Job Family Group:
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Job Family:
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Time Type:
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Most Relevant Skills
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Other Relevant Skills
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The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
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