AI/ML Engineer II

USAA•Phoenix, AZ

4d•Hybrid

About The Position

Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the #1 choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful. The Opportunity Employer: United Services Automobile Association Tasks: Contribute to the development, testing, implementation, and integration of processes with business applications that utilize machine learning model predictions and classifications to inform or drive business activities. Leverage understanding of models and collaborate with Data Scientists to refactor the code into IT maintainable solutions that follows best practices and meets appropriate coding standards. Adhere to and applies ML development standards and coding best practices. Work with cross-functional team to contribute to machine learning projects throughout the machine learning lifecycle to include analysis, solution design, data pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration in support of GenAI applications, ML frameworks/libraires, and ML models. Assist with designing and writing test scripts to verify data integrity and application of functionality. Review functionality of existing test scripts for understanding. Develop familiarity of machine learning engineering best practices by participating in trainings, reviewing documentation, and reading code from existing solutions. Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders, including AWS, GCP, graph databases. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. May allow for partial telecommute.

Requirements

Will accept a Bachelor’s degree in Computer Science, Computer Software, Computer Engineering, Information Technology, Applied Sciences, Mathematics, Physics, or related field followed by 2 years of work experience in job offered or in a related occupation.
Programming Languages: Pandas, NumPy, PySpark, Snowflake SQL, BigQuery, HTML and CSS
Machine Learning & NLP Frameworks: Scikit-learn, XGBoost, TensorFlow, PyTorch, and NLTK
Cloud Platforms & Data Infrastructure: Google Cloud Platform (Vertex AI, BigQuery, and Cloud Storage), Microsoft Azure, AWS (S3 and EC2), Snowflake Snowpark, and OpenShift
Workflow Orchestration & DevOps: Apache Airflow, Control-M, GitLab CI/CD, Docker, FastAPI, Flask, and Domino

Responsibilities

Contribute to the development, testing, implementation, and integration of processes with business applications that utilize machine learning model predictions and classifications to inform or drive business activities.
Leverage understanding of models and collaborate with Data Scientists to refactor the code into IT maintainable solutions that follows best practices and meets appropriate coding standards.
Adhere to and applies ML development standards and coding best practices.
Work with cross-functional team to contribute to machine learning projects throughout the machine learning lifecycle to include analysis, solution design, data pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration in support of GenAI applications, ML frameworks/libraires, and ML models.
Assist with designing and writing test scripts to verify data integrity and application of functionality.
Review functionality of existing test scripts for understanding.
Develop familiarity of machine learning engineering best practices by participating in trainings, reviewing documentation, and reading code from existing solutions.
Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders, including AWS, GCP, graph databases.
Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.