Senior Data Engineer – AI Infrastructure Integration, High Performance Compute

Bank of America

Senior Data Engineer building governed AI/ML solutions for Bank of America’s infrastructure and operations. Developing production models, pipelines, and automation across hybrid cloud and on-premises environments.

Posted 8/19/2026full-timeNew York City • New Jersey, New York, North Carolina • 🇺🇸 United StatesSenior💰 $140,500 - $205,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI/ML model development, including NLP, statistical modeling, and optimization techniques, while effectively managing the full model lifecycle and collaborating across diverse teams. Proficient in Python and familiar with tools such as TensorFlow and scikit-learn to deliver impactful data-driven solutions.

Highest-signal resume keywords

AI/ML Model DevelopmentPython ProgrammingNLP TechniquesMLOps PracticesAgile Delivery

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingPredictive AnalyticsOptimization TechniquesData Pipeline DevelopmentModel Lifecycle ManagementAnomaly DetectionClassification TechniquesFeature EngineeringQuantitative MethodsProduction-Grade Code Development

Soft Skills

Excellent Communication SkillsCollaboration Across TeamsAnalytical ThinkingProblem-Solving

Tools & Technologies

TensorFlowScikit-learnPandasNumPySpaCyHugging Face TransformersGensimJiraConfluenceCI/CD

Industry Keywords

Infrastructure ReliabilityOperational AutomationCapacity ForecastingObservabilityModel RiskData ScienceCloud EngineeringSREAnalyticsAutomation

Tech Stack

Tools & technologies

CloudNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Design, develop, test, validate, and deploy AI/ML-enabled capabilities for infrastructure reliability, capacity forecasting, observability, operational automation, and enterprise decision-making
Apply NLP, statistical modeling, supervised and unsupervised learning, embeddings, classification, anomaly detection, forecasting, and optimization techniques
Build reusable models, data pipelines, APIs, feature workflows, prompt libraries, automation components, dashboards, and integration patterns
Support the full model lifecycle from use-case intake and data preparation through deployment, monitoring, performance review, and remediation planning
Assess model design, assumptions, limitations, performance, controls, explainability, and implementation risks
Partner with infrastructure, data science, model risk, cyber/risk, architecture, operations, and product teams
Define requirements, success metrics, delivery plans, governance artifacts, and operational handoff criteria
Develop production-grade code, documentation, model artifacts, validation evidence, test automation, and implementation procedures
Advance MLOps, CI/CD, version control, model serving, workflow orchestration, monitoring, and hybrid cloud deployment practices
Communicate technical findings, model outcomes, operational impact, risks, and tradeoffs to technical and executive audiences

Requirements

What you’ll need

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 problems
Strong Python programming skills
Practical experience with pandas, NumPy, scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face Transformers, Gensim, or equivalent tools
Experience with the end-to-end model lifecycle
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
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 and quantify business or operational impact through metrics and reporting
Demonstrated experience in Agile delivery environments using Jira, Kanban boards, Confluence, and related platforms
Excellent written and verbal communication skills
Ability to operate across multiple initiatives in a large, matrixed, geographically distributed technology organization
BA or BS in a related quantitative or technical field is listed under Desired Qualifications; advanced Master’s degree preferred

Benefits

Comp & perks

Discretionary incentive eligible; participation in the annual discretionary plan
Annual discretionary award based on individual performance, line of business/group performance, and overall Company success
Benefits eligible
Paid time off
Resources and support for employees’ physical, emotional, and financial wellness