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Emerging Technology Solutions Architect – Machine Learning at 300 U.S. Bank National Association · Katori

Asks for

The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.”
The ad says“Expertise in MLOps practices, including CI/CD pipelines, model versioning, monitoring, governance, and automated retraining.”
The ad says“Experience designing and deploying production machine learning systems in cloud environments.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.”
The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Strong foundation in machine learning, software engineering, and solution architecture.”
The ad says“Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Expertise in MLOps practices, including CI/CD pipelines, model versioning, monitoring, governance, and automated retraining.”
The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.”
The ad says“Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.”
The ad says“Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.”
The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.”
The ad says“Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.”
The ad says“Strong foundation in machine learning, software engineering, and solution architecture.”
The ad says“Strong foundation in machine learning, software engineering, and solution architecture.”
The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.”
The ad says“Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.”
The ad says“Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.”

At U.S. Bank, we’re on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career. Try new things, learn new skills and discover what you excel at—all from Day One.

Job Description

U.S. Bank is seeking an Emerging Technology Solutions Architect – Machine Learning to evaluate, design, and guide adoption of machine learning technologies across the enterprise. This role focuses on identifying emerging ML capabilities, assessing enterprise fit, and defining scalable solutions that enable advanced analytics, predictive modeling, and AI-driven business outcomes while aligning to enterprise standards.

The Emerging Technology Solutions Architect will partner across data engineering, platform engineering, data science, and risk/security teams to evaluate technologies, define architecture patterns, and enable implementation through strong technical leadership and hands-on solution design. This role will help shape the future of machine learning capabilities at U.S. Bank by establishing scalable, secure, and reusable solutions that accelerate responsible innovation.

Responsibilities

Evaluate emerging machine learning technologies, platforms, frameworks, and tooling ecosystems for enterprise adoption.
Assess ML technologies and services including Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, and open-source ML frameworks.
Define scalable architectures supporting the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, deployment, monitoring, and governance.
Recommend architecture patterns based on performance, scalability, security, explainability, and operational risk requirements.
Establish reusable solution patterns for MLOps, model serving, feature stores, automated retraining, model monitoring, and observability.
Design and recommend production-ready machine learning solutions with sufficient technical depth to support engineering and data science teams through implementation.
Evaluate vendor platforms and ecosystem offerings for enterprise fit, long-term viability, and business value.
Partner with data scientists and engineering teams to operationalize machine learning models at scale.
Provide technical leadership on machine learning architecture, MLOps, model lifecycle management, and production deployment strategies.
Establish standards and best practices for model governance, observability, explainability, and responsible AI.
Translate complex technical concepts into clear recommendations for technical and non-technical stakeholders.
Assess emerging machine learning technologies and translate exploratory findings into enterprise-ready recommendations.

Basic Qualifications

Bachelor’s degree or equivalent work experience.
Eight (8) or more years of experience in software engineering, machine learning engineering, data engineering, solution architecture, or related technical roles.

Preferred Skills / Experience

Strong foundation in machine learning, software engineering, and solution architecture.
Experience designing and deploying production machine learning systems in cloud environments.
Expertise in MLOps practices, including CI/CD pipelines, model versioning, monitoring, governance, and automated retraining.
Hands-on experience with machine learning platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow.
Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies.
Experience architecting solutions involving feature stores, model serving, real-time inference, batch scoring, and machine learning pipelines.
Understanding of machine learning concepts including supervised learning, unsupervised learning, forecasting, recommendation systems, anomaly detection, and model explainability.
Experience making architecture decisions grounded in real-world tradeoffs including cost, performance, scalability, security, governance, and model accuracy.
Ability to design solutions and provide technical guidance through implementation, not purely conceptual architecture.
Strong communication, stakeholder alignment, and cross-functional leadership skills.
Familiarity with generative AI and large language models is preferred but not required.

Location Expectation

This role requires working from a U.S. Bank location three (3) or more days per week.

If there’s anything we can do to accommodate a disability during any portion of the application or hiring process, please refer to our disability accommodations for applicants.

Benefits:

Our approach to benefits and total rewards considers our team members’ whole selves and what may be needed to thrive in and outside work. That's why our benefits are designed to help you and your family boost your health, protect your financial security and give you peace of mind. Our benefits include the following:

Healthcare (medical, dental, vision)
Basic term and optional term life insurance
Short-term and long-term disability
Pregnancy disability and parental leave
401(k) and employer-funded retirement plan
Paid vacation (from two to five weeks depending on salary grade and tenure)
Up to 11 paid holiday opportunities
Adoption assistance
Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law

Review our full benefits available by employment status here.

U.S. Bank is an equal opportunity employer. We consider all qualified applicants without regard to race, religion, color, sex, national origin, age, sexual orientation, gender identity, disability or veteran status, and other factors protected under applicable law.

E-Verify

U.S. Bank participates in the U.S. Department of Homeland Security E-Verify program in all facilities located in the United States and certain U.S. territories. The E-Verify program is an Internet-based employment eligibility verification system operated by the U.S. Citizenship and Immigration Services. Learn more about the E-Verify program.

The salary range reflects figures based on the primary location, which is listed first. The actual range for the role may differ based on the location of the role. In addition to salary, U.S. Bank offers a comprehensive benefits package, including incentive and recognition programs, equity stock purchase 401(k) contribution and pension (all benefits are subject to eligibility requirements). Pay Range: $139,230.00 - $163,800.00

U.S. Bank will consider qualified applicants with arrest or conviction records for employment. U.S. Bank conducts background checks consistent with applicable local laws, including the Los Angeles County Fair Chance Ordinance and the California Fair Chance Act as well as the San Francisco Fair Chance Ordinance. U.S. Bank is subject to, and conducts background checks consistent with the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA). In addition, certain positions may also be subject to the requirements of FINRA, NMLS registration, Reg Z, Reg G, OFAC, the NFA, the FCPA, the Bank Secrecy Act, the SAFE Act, and/or federal guidelines applicable to an agreement, such as those related to ethics, safety, or operational procedures.

Applicants must be able to comply with U.S. Bank policies and procedures including the Code of Ethics and Business Conduct and related workplace conduct and safety policies.

Posting may be closed earlier due to high volume of applicants.