AI/ML Engineer - Mid Level

USAA•San Antonio, TX

1d•$127,310 - $243,340•Hybrid

About The Position

USAA is hiring a Mid Level AI/ML Engineer to work across the data management and machine learning lifecycles, including requirements analysis, data collection and processing, statistical analysis, model-performance monitoring, and risk compliance. The role develops and maintains technical systems that use machine-learning models to generate predictions for classification and decision-making. The position partners with Data Scientists and business teams to deliver predictive analytics aligned with business objectives. It involves engineering and integrating machine-learning solutions into business applications; configuring AI/ML infrastructure in cloud and on-premises environments; and supporting GenAI applications, ML frameworks, libraries, and models throughout their lifecycle. The engineer also designs complex solutions and prototypes, contributes to engineering standards and code reviews, and ensures business risks are identified and controlled. The work environment is flexible but requires working in the office four days per week. The role may be based in San Antonio, Texas; Plano, Texas; Phoenix, Arizona; or Charlotte, North Carolina. Relocation assistance is unavailable.

Requirements

Bachelor's degree, or four years of relevant education and/or experience.
Four years of experience in machine-learning engineering, data engineering, or software development implementing data solutions.
Strong programming skills using Python, R, Java, or similar languages.
Strong experience in data engineering and data pipelines.
Experience delivering end-to-end machine-learning solutions, including building, integrating, and optimizing data pipelines; curating and modeling data for different consumers and products, including data marts, data lakes, and operational analytic applications.
Working knowledge of MLOps principles and implementation of automated workflows.
Knowledge of data-science principles and experience with data-science methodologies.
Familiarity with statistical and predictive modeling approaches and machine-learning concepts.
Effective communication skills and ability to work in a team-oriented setting.
Strong experience with at least one cloud platform and its relevant components, such as AWS SageMaker.

Responsibilities

Partner with Data Scientists and business teams to align predictive analytics solutions with business objectives.
Develop, test, implement, and integrate processes with business applications that use machine-learning predictions and classifications to inform or drive business activities.
Collaborate with Data Scientists to refactor model code into maintainable IT solutions that follow best practices and coding standards.
Configure, manage, and set up AI/ML infrastructure in cloud and on-premises environments, including AWS, GCP, and graph databases.
Independently work through the machine-learning engineering lifecycle, including analysis, solution design, data-pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration.
Design and implement complex machine-learning, artificial-intelligence, and GenAI solutions; create proofs of concept and prototypes for complex initiatives.
Collaborate with team members and other engineers to evaluate technologies and alternatives and execute complex assignments.
Help develop, maintain, and enforce company data-development tools and standards.
Conduct regular code reviews to improve quality and ensure compliance.
Identify, measure, monitor, and control risks associated with business activities in accordance with risk and compliance policies and procedures.