Principal AI-Native Software Engineer

This is a full-time onsite role based in the location(s) posted and is performed from the designated work location five days per week. No relocation is offered.

We’re looking for a Principal AI-Native Software Engineer who can turn complex business needs into intelligent, production-ready software.

You’ll work at the intersection of software engineering, agentic AI, LLM-powered applications, and enterprise automation
, partnering directly with business and technical stakeholders to move ideas rapidly from concept to production. You’ll build scalable Python services, APIs, agentic workflows, and integrations that enable intelligent automation across AT&T.

This is not simply an AI experimentation role. We’re looking for an engineer who knows how to combine emerging AI capabilities with proven software engineering practices to deliver solutions that are secure, reliable, observable, maintainable, and built for production
.

• Design and build production-grade AI-enabled applications, backend services, and REST APIs using Python and frameworks such as FastAPI, Flask, or Django.
• Create agentic AI workflows using Lang Graph or similar frameworks, including tool calling, routing, planning and execution, multi-step workflows, and human-in-the-loop patterns.
• Partner directly with business and technical stakeholders in a collaborative 2-in-a-box model
  , translating ambiguous business challenges into technical designs and working solutions.
• Integrate AI applications securely with enterprise APIs, databases, event streams, service gateways, and other internal platforms.
• Design reliable tool and service contracts for AI agents, including schemas, validation, idempotency, retries, rate limits, error handling, and access controls.
• Develop evaluation strategies for AI and agentic systems, including success criteria, regression testing, prompt/tool validation, guardrails, and release gates.
• Implement RAG, vector search, context engineering, structured outputs, and grounding techniques when appropriate to improve accuracy and usability.
• Build end-to-end solutions across backend services, databases, and front-end experiences using technologies such as Angular and other modern web frameworks.
• Apply secure-by-design practices including authentication, authorization, secrets management, least privilege, auditability, input validation, and PII-aware data handling.
• Build production observability with logging, metrics, distributed tracing, dashboards, alerting, and incident troubleshooting
  .
• Implement automated testing, CI/CD, Docker-based deployment, and resilient distributed-system patterns including retries, timeouts, circuit breakers, back pressure, and graceful degradation.
• Use AI coding assistants throughout the engineering lifecycle for code generation, refactoring, testing, debugging, and documentation while maintaining rigorous engineering standards.

• A builder mindset with the ability to move quickly from an ambiguous business problem to a working technical solution.
• Strong understanding of AI-native software development
  , including how and where LLMs and agentic workflows provide meaningful value.
• Hands-on experience developing applications that use LLMs, structured prompting, function/tool calling, output validation, and grounding
  .
• Ability to build agentic solutions that go beyond simple chat interfaces and execute multi-step workflows, use tools, interact with systems, and manage state
  .
• Strong knowledge of software architecture, system design, API design, data modeling, distributed systems, and integration patterns
  .
• Experience designing software for production realities including security, scalability, resiliency, observability, maintainability, and operational support.
• Strong communication…