Senior Data Engineer - AI Infrastructure Integration, High Performance Compute

Job Description:

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!

Position Summary

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.

Key Responsibilities

• 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

Required Qualifications

• 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, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
• Experience with the end-to-end model lifecycle, including model ideation, data preparation, training, selection, validation, deployment, ongoing monitoring, performance review, and governance documentation
• Experience developing NLP, text analytics, classification, embeddings, recommendation, key driver analysis, network analysis, anomaly detection, or predictive modeling solutions
• Experience creating model documentation, validation evidence, implementation procedures, monitoring plans, governance artifacts, or peer review materials in a large enterprise environment
• Working knowledge of APIs, data pipelines, relational databases, SQL, dashboards, visualization tools, automation frameworks, version control, CI/CD, observability, and production support practices
• Ability to analyze complex structured and unstructured data, identify patterns, convert insights into engineering action, and quantify business or operational impact through metrics and reporting
• Demonstrated experience working in Agile delivery environments using tools such as Jira, Kanban boards, Confluence, and related delivery or documentation platforms
• Excellent written and verbal communication skills, with the ability to explain model behavior, technical findings, operational risks, governance requirements, and implementation tradeoffs to technical and executive audiences
• Highly motivated, self-directed, and comfortable operating across multiple initiatives in a large, matrixed, geographically distributed technology organization

Desired Qualifications:           

• BA or BS in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, Artificial Intelligence, Business Analytics, Business Administration, or a related quantitative or technical field; advanced Masters degree preferred
• Experience developing AI/ML solutions for infrastructure operations, capacity forecasting, incident prediction, anomaly detection, root-cause analysis, configuration intelligence, automated remediation, or operational excellence
• Experience with generative AI, large language models, prompt engineering, reusable prompt libraries, AI-assisted workflows, model validation guidance, or GenAI governance practices
• Experience leading or managing data science, NLP, model governance, or AI enablement initiatives across multiple stakeholders or teams
• Experience with enterprise AI infrastructure platforms, model-serving frameworks, GPU or accelerated compute environments, Red Hat OpenShift AI, NVIDIA AI platforms, or comparable AI/ML infrastructure technologies
• Experience integrating AI solutions with enterprise monitoring, observability, workflow orchestration, API, dashboarding, or automation platforms such as Tableau, Streamlit, Shiny, Jupyter, or equivalent tools
• Experience working in regulated environments with model risk management, validation, peer review, data governance, privacy, security, audit, and compliance requirements
• Ability to influence technical direction, establish reusable processes, develop best practices, and communicate effectively with geographically dispersed engineering, operations, architecture, risk, and business partners

Skills:

• Analytical Thinking
• Application Development
• Data Management
• Risk Management
• Solution Design
• Agile Practices
• Architecture
• Collaboration
• Decision Making
• DevOps Practices
• Business Acumen
• Data Quality Management
• Financial Management
• Solution Delivery Process
• Test Engineering

Shift:

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