GenAI​/ML Engineer Finance Tech - Associate Vice President

Role Overview

We are seeking a skilled and passionate GenAI Engineer to join our team in Tampa, FL, and contribute to advancing our AI/ML capabilities within Citi Finance projects. This position focuses on the development and implementation of GenAI solutions, including chatbot development, and working with both structured and unstructured data. The successful candidate will implement cutting‑edge GenAI applications for various financial use cases, leveraging strong Python skills and experience in areas such as RAG pipelines, vector databases, prompt engineering, LLMs (Llama, Gemini, GPT‑4), and dynamic SQL.

• Design, develop, and deploy end‑to‑end GenAI solutions, including chatbot development, for diverse financial applications such as personalized financial advice, fraud detection, risk management, and automated reporting.
• Process and analyze both structured and unstructured data to train and optimize GenAI models, leveraging techniques for information extraction and knowledge representation.
• Collaborate effectively with a team of AI/ML engineers, contributing to expertise in GenAI technologies and best practices, including RAG pipelines, vector databases, prompt engineering, and chatbot development.
• Implement best practices for GenAI development, ensuring code quality, security, maintainability, and scalability of production‑ready applications.
• Collaborate with other technical teams to seamlessly integrate GenAI solutions into existing systems and workflows.
• Utilize text‑to‑SQL to query and retrieve data from relational databases, integrating this data into GenAI pipelines.
• Conduct continuous research and evaluation of emerging GenAI technologies, recommending and implementing relevant solutions.
• Contribute directly to the codebase and participate in technical decision‑making, demonstrating hands‑on technical expertise in GenAI implementation.
• Work closely with stakeholders to understand business needs and translate them into effective technical solutions using GenAI.

• Bachelor’s or Master’s degree in computer science, engineering, or a related field.
• 5–8 years of software engineering experience with a strong focus on AI/ML solutions.
• 2+ years of real‑world experience implementing GenAI solutions, including deploying production‑ready GenAI and chatbot applications.
• Understanding of GenAI models and architectures, with practical experience applying these models to real‑world problems.
• Strong Python programming skills with extensive experience using relevant packages for GenAI and chatbot development.
• Experience processing and analyzing structured and unstructured data using NLP and information retrieval techniques.
• Ability to design and implement RAG pipelines.
• Hands‑on experience with LLMs such as Llama, Gemini, and GPT‑4.
• Proficiency in text‑to‑SQL implementation leveraging LLMs for data extraction and insight generation from relational databases.
• Experience with cloud platforms and MLOps tools.
• Good understanding of agentic AI concepts with POC exposure as an advantage.
• Strong understanding of software engineering principles.
• Excellent communication, collaboration, and problem‑solving skills.
• Experience utilizing AI tools for development and deployment.

• Experience with financial data and applications.
• Familiarity with specific GenAI applications in finance.
• Contributions to open‑source projects related to GenAI or machine learning.

• Programming
  
  Languages:
  
  Python (strong proficiency), Java or Scala.
• Python Packages:
  Transformers, Lang Chain, Lang Graph, Tensor Flow, PyTorch, Pandas, Num Py, Scikit‑learn, Flask/Django, Requests, Beautiful Soup, SQL Alchemy.
• Deep Learning Frameworks:
  Tensor Flow, PyTorch.
• GenAI Libraries / Frameworks:
  Transformers (Hugging Face), Lang Chain, Lang Graph.
• LLMs:
  Llama, Gemini, GPT‑4.
• Vector Database:
  Postgres
  
  DB.
• Cloud Platforms: AWS, Azure, or GCP.
• MLOps Tools: MLflow, Kubeflow.
• Data Science Libraries:
  Pandas, Num Py, Scikit‑learn.
• Containerization:
  Docker, Kubernetes.
• Version Control:
  Git.
• Databases: SQL databases (Postgre
  
  SQL, MySQL, SQL Server) – experience with text‑to‑SQL essential.

Tampa, FL, United…