AI/ML Engineer - Senior Level

USAA•San Antonio, TX

20h•$143,320 - $273,930

About The Position

As a dedicated AI/ML Engineer at the senior level, you will participate in all phases of the data management and machine learning lifecycles, including requirements analysis, data collection and processing, statistical analysis, model performance monitoring, and compliance with related risk requirements. You will develop and maintain technical systems that use machine learning models to make predictions for classification and decision-making. You will consult with data scientists and business partners on machine learning development and implementation, provide technical feedback, lead the full machine learning engineering lifecycle, and configure AI/ML infrastructure in cloud and on-premises environments. You will work with architects on implementation patterns, design complex technical solutions and prototypes, help set technical direction, mentor team members, and ensure business risks are managed in accordance with risk and compliance policies.

Requirements

Bachelor's degree, or four years of relevant education and/or experience.
Six years of machine learning engineering, data engineering, or software development experience implementing data solutions, including two years of demonstrated technical leadership experience and/or leading teams.
Extensive programming experience using Python, Java, or R.
Extensive data engineering experience.
Experience delivering end-to-end machine learning and AI/GenAI solutions, including building, integrating, and optimizing data pipelines and curating and modeling data for different consumers and products, including data marts, data lakes, and operational analytic applications.
Experience completing multiple projects using Agile methodology to implement machine learning models.
Experience leveraging ML Ops principles and implementing automated workflows.
Demonstrated ability to mentor junior technical team members in the area of expertise.
Knowledge of data science principles and methodologies.
Knowledge of stochastic modeling, machine learning, and other advanced mathematical techniques.
Effective communication skills, including the ability to present complex technical concepts to technical and non-technical audiences.
Expert knowledge of at least one cloud platform and its relevant components, such as AWS SageMaker.

Responsibilities

Consult with data scientists and business partners on approaches to machine learning model development and implementation.
Provide technical feedback on machine learning solutions within the technical lead community.
Lead the full machine learning engineering lifecycle, including analysis, solution design, data pipeline engineering, testing, deployment, scheduling, application integration, production support, and API development in support of GenAI applications, ML frameworks and libraries, and ML models.
Configure, manage, and set up AI/ML infrastructure components in cloud and on-premises environments, including AWS, GCP, and graph databases.
Work with architects to influence and define machine learning model implementation patterns in complex environments.
Understand complex model implementation requirements and apply appropriate design patterns throughout solution design and implementation.
Design and implement complex machine learning, artificial intelligence, and GenAI technical solutions.
Create proof of concepts and prototypes for highly complex solutions and present them to leadership.
Assist in setting the team's technical direction and mentor team members.
Ensure business risks are identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.