AI Engineer - Data Scientist I

Bank of America•Charlotte, NY

5d•Onsite

About The Position

The Data and AI team within Global Payment Solutions (GPS) at Bank of America builds end-to-end data and AI solutions for the bank’s payments business. The team works on Generative AI, information retrieval, intelligent agents, machine learning, natural language processing, optimization, and predictive modeling, translating emerging AI capabilities into products that create measurable business value. The AI Engineer will design and build AI-powered products, working across AI research, software engineering, and product development. The role covers the AI application stack, from experimentation and prototyping through evaluation, optimization, deployment, and production monitoring. The engineer will develop intelligent applications using LLMs, RAG, information retrieval, and machine learning, as well as the infrastructure and software needed for reliable, scalable systems in a complex enterprise environment. The position is based at Bank of America Tower, One Bryant Park, New York, NY. It is a first-shift role with 40 hours per week. The position is eligible for an annual discretionary incentive plan and benefits, including paid time off. The stated annual salary range is $107,300 to $193,200; offers are determined based on experience, education, and skill set.

Requirements

Bachelor’s degree or higher in a quantitative or technical field such as computer science, mathematics, statistics, physics, or a related discipline.
At least 2 years of experience developing production-quality software, machine learning systems, or AI-powered applications using Python and/or C++.
Strong foundation in probability, statistics, and applied machine learning, including NLP, time-series analysis, or Generative AI.
Strong understanding of software engineering fundamentals, including modular system design, APIs, testing, version control, and maintainable production code.
Ability to communicate complex technical concepts clearly and concisely to technical and non-technical stakeholders.

Nice To Haves

Graduate degree in computer science, engineering, mathematics, statistics, physics, or another quantitative field.
Hands-on experience building RAG systems, AI agents, LLM applications, or multimodal AI systems.
Exposure to reinforcement learning, recommendation systems, optimization, or time-series modeling.
Comfort with the pace and ambiguity of a dynamic environment and willingness to rapidly learn unfamiliar technical areas.

Responsibilities

Frame abstract business problems using advanced data science and machine learning algorithms.
Own AI systems end-to-end, from initial research and prototyping through architecture, implementation, evaluation, deployment, and ongoing improvement.
Experiment with new models, retrieval strategies, prompting techniques, agent architectures, and emerging AI technologies to determine their applicability to real-world business problems.
Develop rigorous evaluation frameworks to measure AI system quality and identify opportunities for improvement.
Partner with product, engineering, and business stakeholders to translate ambiguous business challenges into well-defined AI solutions.
Shape cross-team collaboration to advance the firm's data science and AI capabilities.