Gen AI-ML Engineer, AVP

Overview of the Role

Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. Our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company, designing digital architecture and ensuring our platforms provide a first‑class customer experience.

We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.

Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well‑rounded backgrounds who bring their authentic selves to work.

The Gen AI‑ML Engineer is an intermediate level position responsible for the establishment and implementation of new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to contribute to application systems analysis and programming activities.

• Design, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting. Explore and experiment with advanced GenAI concepts like Agentic AI.
• Design and implement intelligent chatbots.
• Process and analyze large datasets of structured and unstructured financial data.
• Architect and implement efficient RAG pipelines, leveraging tools like Llama Index and Lang Chain.
• Develop and refine advanced prompting strategies for LLMs.
• Test, evaluate, and analyze the performance of LLM and other GenAI models.
• Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
• Communicate effectively with business stakeholders.
• Stay up to date with the latest advancements in GenAI research and development, including areas like Agentic AI.

• 5+ years of experience in AI/ML development with a proven track record of building and deploying sophisticated GenAI applications.
• Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models. Familiarity with Agentic AI concepts.
• Extensive experience with prompt engineering, fine‑tuning LLMs, and evaluating their performance.
• Expert‑level Python programming skills and proficiency with relevant libraries (Transformers, Lang Chain, Tensor Flow, PyTorch, Pandas, Num Py, Scikit‑learn, Flask/Django, Llama Index).
• Experience with vector databases (Pinecone, Weaviate, Chroma, Faiss, Postgre
  
  SQL with vector extensions) and implementing RAG pipelines using tools like Llama Index and Lang Chain.
• Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).
• Strong analytical, problem‑solving, and communication skills.
• Experience with MLOps principles and tools.
• Excellent collaboration skills.

• Programming
  
  Languages:
  
  Python (expert proficiency required)
• Python Packages:
  Transformers, Lang Chain, Llama Index, Tensor Flow, PyTorch, Pandas, Num Py, Scikit‑learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.
• Deep Learning Frameworks:
  Tensor Flow, Py Torch
• LLMs:
  Llama, Gemini, GPT-4, and other advanced LLMs.
• Vector Databases:
  Pinecone, Weaviate, Chroma, Faiss, Postgre
  
  SQL with vector extensions (pgvector).
• Cloud Platforms: AWS, Azure, GCP.
• MLOps Tools: MLflow, Kubeflow, or similar.
• Containerization:
  Docker,…