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# AI Ops Engineer - Infrastructure

- **Company:** United Services Automobile Association
- **Location:** San Antonio, TX, United States
- **Experience:** Expert
- **Salary:** $114,080.0 - $218,030.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Configuration Management Databases, Software Debugging, Python (Programming Language), Machine Learning, Operational Data Store, Reliability Engineering, Ansible, Tensorflow, Software Deployment, Pytorch, Model Validation, Scikit Learn, Kubernetes, Information Technology, Terraform, AIOps, Dynatrace
- **Published:** October 30, 2026
- **Apply:** https://usaa.wd1.myworkdayjobs.com/USAAJOBSWD/job/San-Antonio-Home-Office-I/Senior-AI-Ops-Engineer---Infrastructure_R0121942/apply

## About the Role

* Bachelor's degree; OR 4 years of relevant education and/or experience.
 * 5+ years of hands-on infrastructure, platform, or site-reliability engineering experience, with strong programming and scripting ability - Python plus Go or a comparable systems language.
 * Practical experience building and productionizing ML models for operational data - anomaly detection, forecasting, or capacity modeling - using frameworks such as TensorFlow, PyTorch, or scikit-learn.
 * Deep, hands-on experience with infrastructure-as-code and orchestration tooling - Terraform, Ansible, Kubernetes operators, or equivalent - for production compute, network, and storage environments.
 * Experience writing and testing operational automation with rollback, compensating actions, and approval-gated or policy-controlled execution for infrastructure changes specifically, not just application deploys.
 * Comfort operating in a regulated environment, with demonstrated ability to debug and reason about a live production infrastructure incident under time pressure.

What sets you apart:

 * Demonstrated experience designing, deploying, and operationalizing model-driven capacity management or anomaly-detection capabilities that support infrastructure operations and on-call engineering teams.
 * Experience developing guard railed automation for production infrastructure, including automated scaling, node replacement, or storage volume expansion, with tested kill-switch and safety-control mechanisms.
 * Experience designing and implementing evaluation frameworks for operational AI, including test cases, regression suites, and drift detection to identify model or threshold degradation prior to production deployment.
 * Experience building or maintaining runtime service or infrastructure topology graphs and reconciling topology data with systems of record, such as a
 * Configuration Management Database (CMDB), with familiarity consuming outputs from enterprise AIOps and observability platforms such as Moogsoft, BigPanda, or Dynatrace.
 * Experience partnering directly with model governance or model risk functions, including developing model cards and supporting documentation to enable model validation, risk assessment, and governance review.

## Description

* Train, validate, and tune unsupervised and time-series models - isolation forests, autoencoders, LSTMs, or comparable techniques - to baseline normal infrastructure behavior and detect capacity and resource anomalies before they become incidents.
 * Build the dynamic service and infrastructure topology graph from CMDB, discovery, and live signal, and design the event correlation logic that reduces raw infrastructure alert volume reaching a human.
 * Write, test, and version remediation automation - Terraform, Ansible, or equivalent - for infrastructure failure modes, with a tested rollback or compensating action for every change, operating within the approval-gate, blast-radius, and kill-switch framework.
 * Instrument and monitor your own models and automations in production, retraining on a defined cadence, and participate in the on-call rotation for any live action at autonomy level 3 or above.