Bank of America
Senior Data Engineer - AI Infrastructure Integration, High Performance Compute
$140K–$205K
New York +2 · Senior · Full-time · Posted Aug 19, 2026
- Location
- New York
Jersey City
Charlotte - Experience
- 15+ years overall; 7+ years hands-on AI/ML or related methods.
- Education
- Desired: BA or BS in a related quantitative or technical field; advanced Masters degree preferred.
About the job
Designs, builds, validates, and operates AI/ML solutions for infrastructure reliability, technology operations, and enterprise decision-making across hybrid cloud and on-premises environments.
What you'll do
- Develop and deploy AI/ML capabilities, data pipelines, APIs, and automation for infrastructure and operational use cases.
- Manage the model lifecycle, including preparation, training, validation, deployment, monitoring, and governance documentation.
- Partner with engineering, operations, risk, and product teams to establish requirements, metrics, and production delivery practices.
What they ask for
- 15+ years delivering data science, software engineering, analytics, automation, platform engineering, risk analytics, cloud engineering, SRE, or infrastructure technology solutions.
- 7+ years hands-on applying AI/ML, NLP, statistical modeling, predictive analytics, optimization, or quantitative methods to enterprise problems.
- Strong Python skills and practical experience with data science, machine learning, or NLP libraries.
- Experience with the end-to-end model lifecycle, enterprise model documentation or governance, and production engineering practices.
Nice to have
- Experience with generative AI, large language models, prompt engineering, or GenAI governance.
- Experience with AI infrastructure platforms, GPU or accelerated compute, Red Hat OpenShift AI, or NVIDIA AI platforms.
Pay
$140,500.00 - $205,000.00 USD annualized base salary
Tools
Python, pandas, NumPy, scikit-learn, TensorFlow, PyTorch, SQL, Jira
AI focus
Applied AI/ML and NLP for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making; includes model validation, deployment, monitoring, and governance.
Model-written summary · the original posting is authoritative