About this opportunity
Amtex Enterprises Inc. lists this Machine Learning Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Job Title : Machine Learning Engineer
Duration: 6-12 plus months
Location: Onsite Role- 5 days onsite
TOS / Tech Hub Locations
Minneapolis/St. Paul “Twin Cities,” MN
Bay Area, CA – San Francisco and surrounding areas
Los Angeles Metro, CA
Charlotte, NC
Chicago, IL
Atlanta, GA
Columbus or Cincinnati, OH
Dallas-Metro, TX
Denver, CO
Milwauke or Oshkos, WI
NYC, NY
Portland,OR
St. Louis, MO
Washington, D.C.
Phoenix/Tempe, AZ
Philadelphia, PA
Owensboro, KY
Kansas City, MO
Knoxville, TN
Fargo, ND
Boston, MA
Role Overview: The AI/ML Platform team at U.S. Bank is seeking a Machine Learning Platform Engineer to design, build, and support machine learning capabilities used by data science teams across the enterprise. This role focuses on MLOps, cloud infrastructure, automation, developer enablement, and production deployment of machine learning workloads.
Key Responsibilities
Core ML Expertise
Lead the development of end-to-end machine learning solutions, including:
Feature engineering
Model training, validation, and evaluation
Design and optimize ML models for performance, scalability, and reliability
Work with platforms such as Azure ML Studio or equivalent ML platforms
Ensure best practices in data preparation, model experimentation, and reproducibility
MLOps & Lifecycle Management
Design and implement production-grade ML pipelines supporting:
Batch processing
Real-time inference
Manage the full MLOps lifecycle, including:
Model deployment and scaling
Model monitoring (data drift, concept drift, performance degradation)
Model versioning and governance
Automated retraining workflows
Establish robust CI/CD pipelines and workflows for ML systems
Ensure reliability, observability, and continuous improvement of ML solutions
Cloud, Platform & Scalability
Architect and deploy ML systems across cloud platforms (Azure, AWS)
Design scalable distributed systems for large-scale data and model processing
Leverage modern infrastructure tools such as:
Containerization (Docker)
Orchestration (Kubernetes)
Infrastructure as Code (Terraform, ARM/Bicep)
Ensure high availability, fault tolerance, and security in production environments
Software Engineering Excellence
Develop robust, maintainable systems using Python
Design and implement microservices-based architectures
Apply secure coding practices and ensure compliance with data protection standards
Enforce software engineering best practices including:
Code reviews
Unit and integration testing
CI/CD pipelines and automation
Technical Leadership & Influence
Provide technical leadership and define ML architecture standards and best practices
Guide critical design decisions for complex ML systems
Mentor and support senior engineers and cross-functional teams
Translate complex business problems into scalable, secure, and resilient ML solutions
Partner with stakeholders to align ML initiatives with strategic business goals
Communication & Execution Skills
Produce clear and comprehensive technical documentation, including:
Architecture diagrams
Design documents
Implementation guides
Conduct architecture walkthroughs for technical and non-technical audiences
Communicate effectively with:
Executive leadership
Business stakeholders
External vendors and partners
Drive execution with strong ownership, prioritization, and delivery focus
Required Qualifications
Strong experience in machine learning development and lifecycle management
Hands-on experience with MLOps practices and production ML systems
Expertise in Python and ML frameworks/libraries
Experience with Azure and/or AWS cloud platforms
Deep understanding of distributed systems and scalable architectures
Proficiency in Docker, Kubernetes, and Infrastructure as Code tools
Demonstrated technical leadership and mentoring experience
Strong written and verbal communication skills
Preferred Qualifications
Experience working with Azure ML Studio or similar platforms
Experience in regulated industries (finance, healthcare, etc.)
Familiarity with advanced monitoring and observability tools
Understanding of AI governance, compliance, and security
Contributions to ML/AI communities or open-source projects
Success Profile
Strategic thinker with strong analytical and problem-solving skills
Passion for building scalable, production-ready ML systems
Ability to bridge technical complexity and business value
Strong collaborator with leadership and cross-functional teams
Execution-focused with a commitment to high-quality delivery
#J-18808-Ljbffr
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.