below marketmost similar roles pay hereabove market
This role pays less than
93%
of similar roles. Most pay
$184,975–$249,000
— the blue band above.
At the midpoint, this role pays about
$152k
versus about
$217k
for comparable roles.
Based on 240 similar postings.
Employer
About US Bank
U.S. Bank (U.S. Bancorp) is the fifth-largest bank in the United States, providing retail banking, corporate and commercial banking, wealth management, and payment services to millions of customers. Industry: Banking & Financial Services
US Bank currently has
44 open roles
on FindRole.
Listed pay typically runs
$119,765–$140,900
across 25 roles with salary data.
The Emerging Technology Solutions Architect – Machine Learning will evaluate, design, and guide the adoption of machine learning technologies across the enterprise. Working with data engineering, platform engineering, data science, and risk teams, this role involves identifying emerging capabilities, defining scalable architecture patterns, and establishing standards for model governance, observability, and explainability. You will build production-ready solutions for the end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, deployment, and monitoring. Key technologies include Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, and Kubeflow, alongside frameworks like PyTorch, TensorFlow, Scikit-learn, and XGBoost. The role solves the business problem of operationalizing machine learning models at scale while ensuring secure, reusable, and responsible innovation within a complex enterprise environment.
Evaluate emerging machine learning technologies, platforms, and frameworks for enterprise adoption.
Define scalable architectures for the end-to-end machine learning lifecycle including data ingestion and model deployment.
Recommend architecture patterns based on performance, scalability, security, and operational risk requirements.
Establish reusable solution patterns for MLOps, model serving, feature stores, and automated retraining.
Design production-ready machine learning solutions to support engineering and data science teams through implementation.
Evaluate vendor platforms and ecosystem offerings for long-term viability and business value.
Provide technical leadership on model lifecycle management and production deployment strategies.
Establish standards and best practices for model governance, observability, and responsible AI.
What we're looking for
Bachelor’s degree or equivalent work experience.
Eight 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 (preferred).
Experience designing and deploying production machine learning systems in cloud environments (preferred).
Expertise in MLOps practices, including CI/CD pipelines, model versioning, monitoring, governance, and automated retraining (preferred).
Hands-on experience with platforms such as Azure Machine Learning, AWS SageMaker, Databricks, Snowflake ML, MLflow, or Kubeflow (preferred).
Knowledge of machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, XGBoost, or similar technologies (preferred).
Familiarity with generative AI and large language models (preferred).
Machine Learning
C++
Python
PyTorch
Apple Silicon
GPU
CPU
Neural Engine
SoC Architecture
Compilers
ML Model Optimization
Hardware IP
Production Inference
Training Environments
Annapolis Junction, MD+131 days ago
$112,800–$257,000
Artificial Intelligence
Machine Learning
Deep Learning
Large Language Models (LLMs)
Agentic AI
DevSecOps
CI/CD
Containerization
Orchestration
Software Design Patterns
Automated Testing
Model Context Protocol (MCP)