Machine Learning Solutions Architect: Emerging Tech

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

Benefits offered by this job

Healthcare (medical, dental, vision)

401(k) and retirement plan

Paid vacation

Job summary

U.S. Bank is seeking an Emerging Technology Solutions Architect – Machine Learning to guide the adoption of ML across the enterprise. You will shape scalable architectures, lead modeling efforts, and partner with data science and engineering teams to deploy at scale.

Responsibilities include evaluating platforms, establishing MLOps patterns, and ensuring security, explainability, and governance while aligning with enterprise standards.

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.

Responsibilities

• Evaluate emerging machine learning technologies, platforms, frameworks, and tooling ecosystems for enterprise adoption.
• Define scalable architectures supporting the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, deployment, monitoring, and governance.
• Establish reusable solution patterns for MLOps, model serving, feature stores, automated retraining, model monitoring, and observability.
• Provide technical leadership on machine learning architecture, MLOps, model lifecycle management, and production deployment strategies.
• Translate complex technical concepts into clear recommendations for technical and non-technical stakeholders.

Skills

ML

Solution architecture

MLOps

Cloud platforms

Python

Communication

Leadership

Education

Bachelor's degree

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 guide the adoption of ML across the enterprise. You will shape scalable architectures, lead modeling efforts, and partner with data science and engineering teams to deploy at scale.

Responsibilities include evaluating platforms, establishing MLOps patterns, and ensuring security, explainability, and governance while aligning with enterprise standards.