Machine Learning Engineer

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