ML Solutions Architect: Emerging Tech & MLOps

U.S. Bank; Hopkins (MN); On-site; USD 139,000 - 164,000; Full time

Job summary

U.S. Bank is seeking an Emerging Technology Solutions Architect – Machine Learning to evaluate, design, and guide adoption of ML technologies across the enterprise.

This role partners with data engineering, platform engineering, data science, and risk teams to define scalable architectures and enable implementation. Join a team driving responsible innovation, establishing reusable patterns for MLOps, model governance, and production ML solutions that accelerate AI-driven business outcomes while

Qualifications

• Eight (8) or more years of experience in software engineering, machine learning engineering, data engineering, solution architecture, or related technical roles.
• Strong foundation in machine learning, software engineering, and solution architecture.
• Hands-on experience with production ML systems in cloud environments and MLOps practices.

Responsibilities

• Evaluate emerging ML technologies, platforms, frameworks, and tooling for enterprise adoption.
• Define scalable architectures for end-to-end ML lifecycle: data ingestion, feature engineering, training, deployment, monitoring, governance.
• Establish reusable patterns for MLOps, model serving, feature stores, automated retraining, monitoring, and observability.
• Provide technical leadership on ML architecture, MLOps, and production deployment strategies.
• Collaborate with data scientists and engineers to operationalize ML models at scale.

Skills

Machine Learning

MLOps

Architecture Leadership

Cloud Platforms

CI/CD

Model Governance

Observability

Python

Education

Bachelor’s degree or equivalent work experience

Tools

Azure Machine Learning

AWS SageMaker

Databricks

Snowflake ML

MLflow

Kubeflow

Job description

U.S. Bank is seeking an Emerging Technology Solutions Architect – Machine Learning to evaluate, design, and guide adoption of ML technologies across the enterprise.

This role partners with data engineering, platform engineering, data science, and risk teams to define scalable architectures and enable implementation. Join a team driving responsible innovation, establishing reusable patterns for MLOps, model governance, and production ML solutions that accelerate AI-driven business outcomes while