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:

1st shift (United States of America)

Hours Per Week:

40

Pay Transparency details

US - NJ - Jersey City - 101 Hudson St - 101 Hudson (NJ2101) US - NY - New York - 1100 Ave Of The Americas - Two Bryant Park (NY1540)

Pay and benefits information

Pay range

$128000.00 - $182300.00 annualized salary offers to be determined based on experience education and skill set.

Discretionary incentive eligible

This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors the performance and contributions of their line of business and/or group; and the overall success of the Company.

Benefits

This role is currently benefits eligible. We provide industry-leading benefits access to paid time off resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.