Data & AI Solutions Analyst - Assistant Vice President

The Data & AI Solutions Analyst is an intermediate-level position responsible for the development of product plans, strategies, and tactics in coordination with the Product , Tech and analytics teams. The overall objective of this role is to coordinate the promotion, development, and deployment of data, analytics, and AI-driven products to create business value, drive adoption, increase market impact, and enhance Citi's competitive position.

Key Responsibilities –

• Design, Develop and deploy generative models (e.g., GPT, GAN, VAEs) for various applications.
• RAG Frameworks – Able to customize and fine-tune existing RAG frameworks or design new RAG to meet project requirements.
• Collaborate with cross-functional team to understand business requirements and translate them into AI solutions.
• Conduct research to advance the state-of-the art in generative modelling and stay up to date with latest advancements in the field.
• Optimize and fine-tune models for performance, scalability, and robustness.
• Implement and maintain AI pipelines and infrastructure to support model training and deployment.
• Perform data analysis and preprocessing to ensure high-quality input for model training.
• Implement and maintain AI pipelines and infrastructure to support model training and deployment. - Perform data analysis and preprocessing to ensure high-quality input for model training.
• Mentor junior team members and provide technical guidance.
• Write and maintain comprehensive documentation for models and algorithms.
• Present findings and project progress to stakeholders and management.

Qualifications –

• Bachelor’s or Master’s degree in computer science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus.
• 8+ years of experience in machine learning and deep learning, with a focus on generative models.
• Strong proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with natural language processing (NLP) and natural language generation (NLG).
• Proven track record of building and deploying generative models in production environments particularly using RAG frameworks.
• Solid understanding of machine learning algorithms, data structures, and software engineering principles.
• Excellent problem-solving skills and the ability to work independently and as part of a team.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Skills –

• Experience with cloud platforms such as AWS, Google Cloud, or Azure. - Knowledge of reinforcement learning and its applications in generative AI.
• Familiarity with MLOps practices and tools.