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AI​/ML Engineer - Mid Level

Job in San Antonio, Bexar County, Texas, 78289, USA
Listing for: USAA - United Services Automobile Association
Full Time position
Listed on 2026-10-22
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist, Data Engineering
Job Description & How to Apply Below
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.

We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs.

The Opportunity As a dedicated AI/ML Engineer – Mid Level, you will engage in all phases of the data management and machine learning life cycles to include requirement analysis, collection, processing, statistical analysis of data, monitoring of model performance, and adherence to related risk requirements. You will develop and maintain technical systems that use machine learning models to make predictions used for classification and/or decision making.

We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations:
San Antonio, TX, Plano, TX, Phoenix, AZ, or Charlotte, NC. Relocation assistance is not available for this position.

What you will do:

Partner with Data Scientists and business to ensure predictive analytics solutions aligned to business objectives.

Develops, tests, implements, and integrates 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.

Configure, manage, and set up AI/ML infrastructure components in cloud/on-prem environments for projects and the AI/ML community stakeholders. This includes AWS, GCP, graph databases.

Independently conducts work in the full life cycle of machine learning engineering 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/libraries, and ML models.

Designs and implements complex technical solutions for machine learning, artificial intelligence, GenAI, Creates proof of concepts and prototypes that drive the vision and the outcome for complex initiatives to be delivered.

Collaborates with the team and other engineers on new technologies and alternatives to plan and execute complex assignments and tasks.

Assists to develop, maintain, and enforce the company's data development tools and standards. Conducts code reviews on a regular basis to improve quality and ensure compliance.

Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

What you have:

Bachelor's degree; OR 4 years of relevant education and/or experience.
4 years of machine learning engineering, data engineering, or software development experience implementing data solutions.

Strong programming skills using Python, R, Java, etc.

Strong experience in data engineering and data pipelines.

Experience with end to end delivering of machine learning solutions that includes building, integrating, and optimizing data pipelines; curating and modeling the data for various types of consumers/products and their pattern of usage to include data marts, data lake, and operational analytic applications.

Working knowledge of ML Ops principles…
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