AI/ML Engineer - Mid Level

USAA•San Antonio, NC

5d•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 controls. The engineer develops and maintains technical systems that use machine learning predictions for classification and decision-making. This role partners with Data Scientists and business stakeholders to align predictive analytics with business objectives. It delivers machine-learning solutions and GenAI applications from analysis and design through data-pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration. The engineer also configures AI/ML infrastructure in cloud and on-premises environments, develops prototypes for complex initiatives, supports engineering standards, and conducts code reviews. The work environment is flexible but requires four days per week in the office. The position may be based in San Antonio, Texas; Plano, Texas; Phoenix, Arizona; or Charlotte, North Carolina. Relocation assistance is not available. USAA does not provide visa sponsorship for this role.

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 in languages such as Python, R, or Java.
Strong experience in data engineering and data pipelines.
Experience delivering end-to-end machine-learning solutions, including building, integrating, and optimizing data pipelines and curating and modeling data for consumers and products, including data marts, data lakes, and operational analytic applications.
Working knowledge of MLOps principles and implementing 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 effectively 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 stakeholders 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.
Conduct the full 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 technical solutions for machine learning, artificial intelligence, and GenAI.
Create proofs of concept and prototypes that advance the vision and outcomes of complex initiatives.
Collaborate with teams and engineers to evaluate new 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.