Senior Data Engineer - AI Infrastructure Integration, High Performance Compute

Artificial Intelligence Sr. Data Engineer

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth.

We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates' physical, emotional, and financial wellness through affordable, competitive and flexible benefits. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences.

These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact.

Join us!

The Artificial Intelligence (AI) Sr. Data Engineer will design, build, validate, and operationalize AI-enabled solutions that improve infrastructure, technology operations, and enterprise decision-making across hybrid cloud and on-premises environments. The role partners with infrastructure engineering, architecture, operations, cyber/risk, model governance, data science, and product teams to convert business and technology needs into secure, scalable, measurable capabilities. The ideal candidate combines applied data science, natural language processing, machine learning, automation, model validation, and software engineering experience with the discipline to deliver production-ready solutions in a regulated enterprise environment.

This role requires strong technical execution, governance awareness, stakeholder communication, and the ability to move AI/ML capabilities from concept through deployment, monitoring, and continuous improvement.

• Design, develop, test, validate, and deploy AI/ML-enabled capabilities that improve infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
• Apply natural language processing, statistical modeling, supervised learning, unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques to complex enterprise data sets
• Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns across technology, risk, operations, and platform domains
• Support the full model lifecycle, including use case intake, data preparation, model training, model selection, validation readiness, deployment, monitoring, ongoing performance review, and remediation planning
• Provide analytical and technical challenge to AI/ML solutions by assessing model design, assumptions, limitations, performance, controls, explainability, and implementation risks
• Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams to define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
• Develop production-grade code, reusable documentation, model artifacts, validation evidence, test automation, and implementation procedures aligned to enterprise engineering and governance standards
• Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices for AI-enabled infrastructure services
• Communicate technical findings, model outcomes, operational impact, implementation risks, and tradeoffs clearly to engineering teams, senior stakeholders, governance partners, and cross-functional leaders

• 15+ years of experience delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions
• 7+ years of hands-on experience applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise business, risk, technology, or operational problems
• Strong Python programming skills and practical experience with data science, machine learning, or NLP libraries such as pandas, Num Py, scikit-learn, Tensor Flow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
• Experience with the…