AI/ML Engineer II

USAA•Phoenix, AZ

7d•Hybrid

About The Position

USAA is seeking an AI/ML Engineer II to contribute to the development, testing, implementation, and integration of processes that use machine learning model predictions and classifications to inform or drive business activities. This role involves leveraging an understanding of models and collaborating with Data Scientists to refactor code into IT maintainable solutions that follow best practices and meet appropriate coding standards. The engineer will adhere to and apply ML development standards and coding best practices, and work with cross-functional teams throughout the machine learning lifecycle, including analysis, solution design, data pipeline engineering, testing, deployment, scheduling, production support, API development, and application integration for GenAI applications, ML frameworks/libraries, and ML models. Responsibilities also include assisting with designing and writing test scripts, reviewing existing test scripts, developing familiarity with machine learning engineering best practices, and configuring/managing AI/ML infrastructure components in cloud/on-prem environments (AWS, GCP, graph databases). The role also requires ensuring risks are identified, measured, monitored, and controlled according to policies and procedures. Partial telecommuting may be allowed.

Requirements

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.