Senior AI Ops Engineer - Application at USAA in San Antonio Home Office I

Job description

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 The Application AI Ops team is where machine learning meets the business logic customers touch — the services, APIs, and transaction flows that move money and keep accounts current. As a Senior AI Ops Engineer on this team, you won’t be prototyping in a notebook and handing it off for someone else to ship: you’ll build the ingestion pipelines that turn application logs, traces, and business-transaction signals into features; train and tune the models that catch a degrading payment API or an anomalous checkout failure before a customer calls in; and then write the deterministic, tested automation that acts on what those models find — a guided remediation, a safe auto-restart, a rollback that actually rolls back. This is a from-scratch build inside a regulated financial institution, which means your code will eventually touch production systems that move real money, under real audit scrutiny — and the engineering bar reflects that. This position offers a flexible work environment with an onsite requirement of 5 days per week. This position is based in San Antonio, TX. Relocation assistance is not available for this position. What you'll do: Build and operate high-throughput ingestion and feature pipelines that consume application logs, traces, APM data, and business-transaction events — collected by the Observability & Telemetry team — into the AIOps feature store. Train, validate, and tune anomaly-detection and forecasting models against application and transaction-level signals, and build LLM-based log parsing, summarization, and root-cause-hypothesis components with explicit handling for hallucination risk. Design and build event correlation and probable-cause ranking logic that reduces raw application alert volume reaching a human. Write, test, and version remediation automation for application-layer failure modes, with tested rollback for every action, 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. undefined What you have: Bachelor's degree; OR 4 years of relevant education and/or experience. 5+ years of hands-on software or data engineering experience, with strong programming ability in Python plus a second production language — code that ships, is tested, and is code-reviewed, not just notebooks. Practical experience building and productionizing ML models — anomaly detection, classification, or forecasting — using frameworks such as TensorFlow, PyTorch, or scikit-learn, including the full lifecycle from training through retraining. Working fluency with LLM-based techniques — prompt design, retrieval-augmented generation, structured outputs — and a clear-eyed understanding of their failure modes. Experience writing and testing operational automation — workflow orchestration, runbook-as-code — including rollback design and approval-gated or policy-controlled execution. Comfort operating in a regulated environment, with demonstrated ability to debug and reason about a live production incident under time pressure. What sets you apart: Proven experience deploying model-driven root-cause analysis or incident triage solutions used in production application operations. Exp