Lead Cybersecurity – Application Security Architect, AI Models, Frameworks & Implementation

AT&T

Application Security Architect securing AI/ML application designs and integrations for AT&T. Bridging application security and AI engineering to reduce vulnerabilities and risks.

Posted 5/6/2026full-timeCharlotte • New Jersey, North Carolina, Texas, Washington • 🇺🇸 United StatesSenior💰 $128,400 - $192,600 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformJavaScriptPythonPyTorchSDLCTensorflowTypeScript

About the role

Key responsibilities & impact

Design, review, and validate secure architectural patterns for AI/ML and LLM-enabled applications, including locally hosted models, cloud-native AI services, API-based model access, RAG systems, and agent-based workflows.
Define secure reference architectures for AI integrations across applications, services, and platforms.
Ensure security is embedded into AI solution design from the start, including trust boundaries, identity controls, data flows, model access, and output handling.
Advise teams on secure use of frameworks such as Azure AI Foundry, LangChain, Semantic Kernel, OpenAI/Azure OpenAI integrations, and similar orchestration or inference technologies.
Lead threat modeling sessions for AI-enabled applications and platforms to identify abuse cases, architectural weaknesses, and control gaps.
Assess risks such as prompt injection, model evasion, data poisoning, jailbreaks, model inversion, model extraction, tool misuse, and unauthorized privilege escalation through agent workflows.
Conduct technical security reviews of AI applications, integrations, and architectures with clear remediation recommendations and risk prioritization.
Translate AI threat scenarios into practical mitigations that development and engineering teams can implement.
Define and implement AI-specific security guardrails, including prompt/input filtering, context validation, output sanitization, response validation, policy enforcement, model/tool access restrictions, and sensitive data handling controls.

Requirements

What you’ll need

7+ years of experience in application security, product security, security architecture, or secure software engineering, with at least 2–3 years focused on AI/ML or LLM security, AI-enabled application architecture, or adversarial AI security.
Strong background in application security principles and methodologies, including secure design review, threat modeling, vulnerability management, API security, authn/authz, and secure SDLC practices.
Demonstrated experience securing AI/ML systems, LLM-enabled applications, or AI integration patterns in enterprise or production environments.
Practical experience with AI models, frameworks, and orchestration technologies, such as Azure AI Foundry, Azure OpenAI/OpenAI APIs, LangChain, Semantic Kernel, Hugging Face, TensorFlow, PyTorch, or similar ecosystems.
Hands-on experience implementing security controls for AI use cases, including prompt filtering, output validation, model access controls, data protections, agent/tool guardrails, and monitoring.
Strong understanding of AI-specific threats such as prompt injection, jailbreaks, model inversion, data poisoning, model extraction, insecure plugins/tools, and sensitive data leakage.
Demonstrated ability to write, review, and implement code when needed, including scripting, prototyping, automation, integrating security controls into applications and CI/CD pipelines, and building practical solutions to support AppSec and AI security use cases.
Proficiency in one or more programming/scripting languages such as Python, JavaScript/TypeScript, Go, or Bash; Python strongly preferred, with the ability to work comfortably in existing codebases, automation scripts, and integration layers.
Experience working with cloud-native platforms and services (Azure preferred; AWS/GCP also valuable), including APIs, containers, IAM, secrets management, logging, and deployment pipelines.
Strong familiarity with AI and AppSec frameworks such as OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, and secure architecture principles for AI systems.
Practical experience working with source code repositories and modern development workflows, including branching, pull requests, code review, repository hygiene, and CI/CD integration.
Experience using or supporting GitHub-based development environments, including repository management, Git-based workflows, and security integration into build and deployment pipelines.
Familiarity with artifact, package, and binary repository management, including platforms such as JFrog Artifactory, to support secure handling of dependencies, build artifacts, containers, models, or related software assets.
Strong communication skills with the ability to work across engineering, architecture, data science, security, risk, and leadership stakeholders.

Benefits

Comp & perks

Medical/Dental/Vision coverage
401(k) plan
Tuition reimbursement program
Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company-designated holidays)
Paid Parental Leave
Paid Caregiver Leave
Additional sick leave beyond what state and local law require may be available but is unprotected
Adoption Reimbursement
Disability Benefits (short term and long term)
Life and Accidental Death Insurance
Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
Employee Assistance Programs (EAP)
Extensive employee wellness programs
Employee discounts up to 50% off on eligible AT&T mobility plans and accessories, AT&T internet (and fiber where available) and AT&T phone.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

application securitysecurity architecturesecure software engineeringAI/ML securitythreat modelingvulnerability managementAPI securitysecure SDLCprogramming languagessecurity controls implementation

Soft Skills

strong communication skillscollaborationproblem-solvingleadershipadvisory skillsrisk prioritizationtechnical writingreview and implementation