Enterprise ML Solutions Architect: AI & MLOps Lead

Usbank; Charlotte (NC); On-site; USD 139,000 - 164,000; Full time

Benefits offered by this job

Healthcare

Life insurance

Disability insurance

Parental leave

401(k)

Paid vacation

Holiday opportunities

Adoption assistance

Sick leave

Job summary

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

You will partner with data science, data engineering, and risk teams to define scalable, production-ready ML solutions while upholding enterprise standards. Responsibilities include evaluating platforms like Azure ML, SageMaker, Databricks and Snowflake ML, designing end‑to‑end ML architectures, and leading MLOps practices to enable responsible

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.

Skills

Machine learning

Solution architecture

MLOps

CI/CD

Model monitoring

Security

Cost optimization

Communication

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 across the enterprise.

You will partner with data science, data engineering, and risk teams to define scalable, production-ready ML solutions while upholding enterprise standards. Responsibilities include evaluating platforms like Azure ML, SageMaker, Databricks and Snowflake ML, designing end‑to‑end ML architectures, and leading MLOps practices to enable responsible