Lead AI-Native Software Engineer for Production

AT&T

Plano (TX)

On-site

USD 155,000 - 261,000

Full time

3 days ago

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Benefits offered by this job

Health coverage

401(k) plan

Tuition reimbursement

Paid time off

Job summary

AT&T is seeking a Principal AI-Native Software Engineer to 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, building scalable Python services, APIs, and agentic workflows.

This role emphasizes secure, observable, maintainable systems with production-grade practices, tool calling, and multi-step AI workflows.

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related technical discipline or equivalent practical experience.
Strong Python development experience with modern frameworks (FastAPI, Flask, or Django).
Hands-on experience designing and developing REST APIs and backend/microservice architectures.
Experience integrating applications with enterprise platforms, databases, APIs, or distributed systems.
Strong knowledge of SQL and relational databases; NoSQL/document databases are beneficial.
Experience with unit testing, integration testing, automated testing, CI/CD, code review, and refactoring.
Experience with Docker and deployment into cloud or enterprise environments.
Working knowledge of production monitoring for logging, metrics, tracing, incident troubleshooting.
Understanding of secure API development, authentication/authorization, secrets management, least privilege, and audit logging.
Hands-on experience integrating LLMs or Generative AI capabilities into software applications.
Experience with tool/function calling, structured prompting, output validation, RAG, retrieval, or grounding techniques.
Demonstrated use of AI coding assistants as part of the software development lifecycle.

Responsibilities

Design and build production-grade AI-enabled applications, backend services, and REST APIs using Python and frameworks like FastAPI, Flask, or Django.
Create agentic AI workflows with LangGraph or similar frameworks, including tool calling, routing, planning and execution, multi-step workflows and human-in-the-loop patterns.
Collaborate with business and technical stakeholders in a 2-in-a-box model to translate ambiguous problems into technical designs.
Integrate AI applications securely with enterprise APIs, databases, event streams, service gateways, and 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 where appropriate.
Build end-to-end solutions across backend services, databases, and front-end experiences using technologies like Angular and modern web frameworks.
Apply secure-by-design practices including authentication, authorization, secrets management, least privilege, auditability, input validation, and PII 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 (retries, timeouts, circuit breakers, backpressure, graceful degradation).
Use AI coding assistants throughout the engineering lifecycle for code generation, refactoring, testing, debugging, and documentation while maintaining standards.