Principal AI Engineer – SDLC

AT&T

. Design, develop, and deploy AI-powered applications and platforms supporting enterprise business objectives .

Posted 9/17/2026full-timeDallas • Texas • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying AI-powered applications, with a strong focus on Generative AI technologies, cloud platforms, and modern software engineering practices. Proficient in building scalable APIs and microservices while implementing MLOps and CI/CD pipelines for efficient AI solutions.

Highest-signal resume keywords

Python ProgrammingGenerative AI TechnologiesCloud Platforms (Azure, AWS)MLOps PracticesContainerization (Kubernetes, Docker)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Application DevelopmentRESTful API DevelopmentMicroservices ArchitectureAgile MethodologiesSoftware Engineering PracticesAI WorkflowsScalable Cloud-Native ApplicationsCI/CD Pipeline ImplementationDevOps PracticesEnterprise Application Integration

Soft Skills

Problem-SolvingAnalytical SkillsCollaboration Skills

Tools & Technologies

KubernetesDockerAzureAWSVector DatabasesAI Evaluation Frameworks

Industry Keywords

AI Automation SolutionsIntelligent AssistantsAgent-Based WorkflowsProduction-Ready SoftwareEnterprise SecurityGovernanceResponsible AI Practices

Tech Stack

Tools & technologies

AWSAzureCloudDockerKubernetesMicroservicesPythonSDLC

About the role

Key responsibilities & impact

Design, develop, and deploy AI-powered applications and platforms supporting enterprise business objectives
Build Generative AI solutions using LLMs, RAG architectures, and agent-based workflows
Develop intelligent assistants, copilots, chatbots, and AI automation solutions
Transform AI concepts, proofs of concept, and prototypes into production-ready software products
Design and build scalable APIs, microservices, and backend services
Integrate AI capabilities into enterprise platforms, business systems, and customer-facing applications
Develop secure, reliable, and reusable components within modern software architectures
Translate business requirements into technical solutions with cross-functional teams
Deploy and manage AI workloads across Azure and AWS
Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
Build and maintain enterprise-grade AI platforms for high-volume production workloads
Optimize performance, reliability, security, and scalability of AI services
Establish and maintain CI/CD pipelines for AI-enabled applications and services
Implement MLOps practices for deployment, monitoring, testing, and lifecycle management
Support model integration, version management, governance, and operational excellence
Monitor production environments and improve platform performance and user experience
Evaluate emerging AI technologies and opportunities for enterprise adoption
Contribute to technical architecture decisions and AI engineering best practices
Drive continuous improvement across AI development methodologies and delivery frameworks

Requirements

What you’ll need

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field
Experience developing enterprise applications using modern software engineering practices
Strong proficiency in Python and modern application development frameworks
Experience building RESTful APIs and microservices
Knowledge of Generative AI technologies, LLMs, and AI application architectures
Experience with cloud platforms such as Azure and/or AWS
Experience working within Agile and SDLC environments
Knowledge of CI/CD pipelines, DevOps practices, and software release management
Experience with containerization and orchestration technologies such as Kubernetes and Docker
Strong problem-solving, analytical, and collaboration skills
Experience building RAG solutions
Experience developing agentic AI workflows and autonomous AI systems
Knowledge of MLOps platforms and machine learning deployment practices
Experience integrating AI solutions into enterprise business systems
Familiarity with vector databases, prompt engineering, and AI evaluation frameworks
Experience developing scalable cloud-native AI applications
Exposure to enterprise security, governance, and responsible AI practices

Benefits

Comp & perks

Equal employment opportunity and nondiscrimination commitment
Reasonable accommodations for qualified individuals with disabilities
Fair chance hiring process; background checks begin after an offer is made
40 weekly hours
Regular time type