Emerging Technology Solutions Architect – Machine Learning

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 Sage Maker, 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 vide 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 Sage Maker, Databricks, Snowflake ML, MLflow, or Kubeflow.

Knowledge of machine learning frameworks including PyTorch, Tensor Flow, 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…