Context Engineer

USAA•Charlotte, NC

About The Position

USAA is seeking a Context Engineer to work at the intersection of business strategy, data science, and engineering, structuring business context, data, and logic to support generative AI, LLMs, and autonomous agents. The role focuses on designing prompts, workflows, and agent behaviors; iterating on AI solutions; optimizing model outputs; and ensuring safe, compliant agent control design. The Context Engineer collaborates with business, data science, and engineering teams to translate business objectives into AI and agentic workflows. The role designs and evaluates prompt architectures, context windows, and retrieval-augmented generation pipelines; engineers and monitors AI agents; and applies evaluation frameworks to assess model behavior and output quality. It also involves documenting AI systems and tests, communicating findings to stakeholders, adopting emerging AI technologies, and identifying and controlling operational, data leakage, and automation risks.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, or a related technical or quantitative field; alternatively, four years of relevant education and/or technical experience.
At least two years of hands-on experience with modern AI/ML technologies, data analytics, or software development involving LLM applications, prompt engineering, or workflow automation; alternatively, an advanced degree in Computer Science, AI, Data Science, or a related quantitative or technical discipline.
Demonstrated experience with LLMs, prompt engineering patterns such as Chain-of-Thought and few-shot prompting, and major foundation model APIs or open-weights models.
Practical familiarity with AI orchestration frameworks and toolsets such as LangChain, LlamaIndex, Semantic Kernel, or custom agent frameworks, as well as vector databases.
Proficiency in Python and modern data manipulation tools, and ability to query structured and unstructured data sources using SQL and APIs to provide context for AI pipelines.
Solid understanding of AI risk management, guardrails, security best practices such as mitigating prompt injection and protecting data privacy, and agent control design.

Responsibilities

Collaborate with business, data science, and engineering teams to translate business objectives into AI and agentic workflows.
Design, test, and optimize prompt architectures, context windows, and retrieval-augmented generation pipelines to improve LLM performance and accuracy.
Engineer and monitor autonomous or semi-autonomous AI agents, iterating on solution design and refinement.
Evaluate model behavior, hallucinations, latency, and output quality using qualitative and quantitative frameworks tied to business use cases.
Follow risk, safety, and agent control design standards to support ethical, aligned, and compliant AI deployment.
Synthesize AI outputs and multimodal data insights into actionable recommendations and communication materials for peer review and stakeholder alignment.
Document system instructions, prompt templates, agent logic, and testing frameworks to support transparent reviews and compliance audits.
Explore and adopt emerging generative AI technologies, frameworks, and tools to enhance solution strategies.
Present findings and demonstrate agent capabilities to influence mid-level management, gain stakeholder support, and accelerate AI adoption.
Identify, measure, monitor, and control risks involving AI operations, data leakage, and automated business activities.