AI-DLC Engineering Lead

USAA•San Antonio, TX

1d•Hybrid

About The Position

USAA is seeking an AI-DLC Engineering Lead to provide hands-on technical leadership for the association-wide transformation of the software development lifecycle. This role will report directly to the executive accountable for AI-DLC and will focus on advancing the delivery model from AI-assisted development to an AI-driven delivery model. The lead will guide the engineering discipline for AI-DLC, translate transformation needs into technical requirements, and lead the AI Platform engineering team in delivering reusable capabilities. This involves working across various departments including Lines of Business, Association Functions, architecture, IT Governance, API Management, security, risk, and engineering teams to integrate AI-driven workflows into established software delivery practices. This is a hands-on leadership role requiring the ability to connect strategy to implementation, establish technical direction, build reference implementations, evaluate new approaches, resolve technical challenges, and drive improvements in platform, tooling, and processes based on feedback. The role demands deep expertise in software engineering, cloud, DevSecOps, and SDLC, along with practical experience in implementing AI coding agents, agentic workflows, specification-driven development, human approval gates, and production controls. AI-DLC encompasses AI's application beyond code generation to include requirements, architecture, planning, testing, deployment, operations, and maintenance.

Requirements

Bachelor's degree in a related field; OR four years of relevant education and/or experience.
Eight years of broad software engineering, platform engineering, cloud engineering, DevOps, developer productivity, or related technology experience.
Three years of demonstrated technical leadership for complex, cross-functional, portfolio-level, or enterprise engineering initiatives.
Six years of experience delivering technology solutions across multiple phases of the software development lifecycle, including requirements, architecture, design, implementation, testing, deployment, production support, modernization, and optimization.
Experience leading an enterprise transformation involving AI-assisted development, agentic software engineering, AI-native development, or AI-driven software delivery.
Experience implementing AI across multiple SDLC phases rather than limiting AI use to code completion or individual developer productivity.
Extensive experience designing and implementing secure, resilient, scalable, observable, and maintainable software platforms or distributed systems used by multiple engineering teams.
Demonstrated ability to establish technical direction, reference architectures, engineering standards, reusable patterns, platform capabilities, and production-readiness practices.
Advanced software engineering experience in Python or another modern programming language, including APIs, integrations, automation, distributed systems, testing, and production-quality services.
Advanced experience with public cloud architecture, DevSecOps, Infrastructure as Code, CI/CD, automated testing, observability, incident response, and Site Reliability Engineering.
Demonstrated experience integrating new engineering capabilities into established software development processes, delivery pipelines, security controls, and operational practices.
Demonstrated ability to lead architecture, code, design, security, control, and implementation reviews and resolve complex cross-system technical issues.
Demonstrated ability to translate ambiguous transformation needs into technical strategy, prioritized requirements, solution architectures, implementation roadmaps, and measurable outcomes.
Demonstrated experience influencing senior leaders and communicating complex technical decisions, risks, dependencies, sequencing considerations, and recommendations across organizational boundaries.
Strong business acumen in technology investment, engineering productivity, operational management, cost optimization, risk management, and organizational change.
Experience mentoring experienced engineers and leading technical work across teams without relying exclusively on direct reporting authority.

Nice To Haves

Experience leading an enterprise transformation involving AI-assisted development, agentic software engineering, AI-native development, or AI-driven software delivery.
Experience implementing AI across multiple SDLC phases rather than limiting AI use to code completion or individual developer productivity.
Hands-on experience with AI coding and software engineering tools such as GitHub Copilot, Amazon Q Developer, Kiro, Claude Code, or comparable enterprise development assistants.
Experience designing workflows where AI develops plans, requirements, specifications, code, tests, documentation, and other engineering artifacts while people retain decision authority at defined gates.
Experience with specification-driven development, structured requirements, repository-based context engineering, reusable agent instructions, coding standards, and machine-readable engineering artifacts.
Experience creating or integrating specialized coding agents, custom agents, agent skills, agent tools, reusable prompts, repository instructions, hooks, and workflow steering rules.
Experience integrating AI agents with repositories, issues, pull requests, code review, automated testing, security scanning, build pipelines, deployment pipelines, and release controls.
Experience with Amazon Bedrock, Amazon Bedrock AgentCore, or comparable services for foundation-model access, agent runtime, identity, tool connectivity, memory, and observability.
Experience with agent orchestration frameworks such as LangGraph, AWS Strands Agents, LangChain, Semantic Kernel, or comparable frameworks.
Experience implementing Model Context Protocol, secure agent tools, API-based integrations, agent identity, delegated authorization, least-privilege access, or sandboxed execution.
Experience designing human-in-the-loop controls, approval of workflows, automated evaluations, release thresholds, audit trails, traceability, and evidence collection for AI-generated engineering outputs.
Experience integrating AI-enabled delivery with platforms such as GitHub, GitLab, Jira, Confluence, Rovo, ServiceNow, API management platforms, knowledge repositories, workflow engines, and developer portals.
Experience with IBM watsonx.governance or comparable capabilities supporting AI inventory, risk management, evaluations, monitoring, controls, exceptions, and governance evidence.
Experience defining measures for AI-DLC adoption and performance, including cycle time, throughput, quality, rework, defect escape, automated task completion, human intervention, control effectiveness, developer experience, and cost.
Experience conducting forward-deployed or embedded engineering engagements that validate solutions with delivery teams and convert repeatable patterns into shared capabilities.
Experience building reusable services, APIs, templates, SDKs, platform components, and implementation accelerators.
Experience leading technical change across a federated enterprise where common capabilities must support specialized business and technology needs.
Demonstrated ability to remain hands-on while influencing engineering teams, architects, governance partners, product leaders, and senior executives.

Responsibilities

Lead the engineering discipline and provide hands-on technical direction for association-wide AI-DLC adoption.
Translate AI-DLC transformation needs into prioritized engineering requirements and guide the AI Platform engineering team.
Design AI-driven workflows across requirements, architecture, planning, implementation, testing, security validation, release authorization, deployment, and production feedback.
Establish human decision gates, engineering standards, reference architectures, reusable patterns, and production-readiness expectations.
Build reference implementations and integrate AI-DLC with repositories, development environments, CI/CD pipelines, testing, security, change, release, and monitoring capabilities.
Optimize platform capabilities through reusable services, components, APIs, agent tools, templates, instructions, evaluation frameworks, and integration patterns.
Partner with Lines of Business and Association Functions to deliver consistent capabilities with extensibility points for specialized vertical needs.
Partner with IT Governance to embed policies, standards, controls, approvals, evidence collection, and exception handling into AI-driven workflows.
Partner with API Management to promote reusable APIs and services, improve the software development tooling stack, and enable scalable access to enterprise capabilities.
Apply AWS, software engineering, cloud, DevSecOps, and Site Reliability Engineering expertise to integrate AI-driven workflows into established delivery practices.
Define secure patterns for agent context, tools, APIs, knowledge, identity, permissions, execution environments, failure handling, and output validation.
Develop automated evaluations and quality gates for AI-generated requirements, designs, code, tests, security findings, and release artifacts.
Use implementation feedback and production insights to reduce manual effort, rework, duplication, and inconsistent practices.
Define measures for adoption, engineering capacity, cycle time, quality, automation, control effectiveness, developer experience, and business outcomes.
Guide technical decisions involving architecture, dependencies, risks, governance, integrations, platform constraints, and capability sequencing.
Lead cross-functional engineering efforts, mentor engineers, and serve as a trusted technical advisor to the executive accountable for AI-DLC.
Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.