Senior Data Engineer - AI Infrastructure Integration, High Performance Compute

Bank of America•Jersey City, NY

21h•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

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