Lead Cybersecurity - Application Security Architect - AI Models, Frameworks & ImplementationAT&T • Bedminster, New Jersey, United States

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

AT&T

Bedminster, New Jersey, United States

Bedminster, New Jersey, United States

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Overview This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered. Join AT&T and reimagine the communications and technologies that connect the world. Our Chief Security Office ensures that our assets are safeguarded through truthful transparency, enforce accountability and master cybersecurity to stay ahead of threats. Bring your bold ideas and fearless risk-taking to redefine connectivity and transform how the world shares stories and experiences that matter. When you step into a career with AT&T, you won’t just imagine the future—you’ll create it. Job Summary: The Application Security Architect is responsible for defining and driving secure-by-design approaches for AI-enabled applications and services. This role focuses on protecting the full lifecycle of AI/ML systems, including LLM-based applications, agentic workflows, RAG pipelines, model APIs and inference services, training/fine-tuning pipelines, and third-party AI integrations and SaaS capabilities. It combines application security architecture with AI security engineering to reduce risk across the full AI lifecycle and lead AI Security from a vulnerability management and risk-reduction perspective. Responsibilities 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 AI-specific risks such as prompt injection, model evasion, data poisoning, jailbreaks, model inversion, model extraction, tool misuse, and unauthorized privilege escalation through agent workflows. Define and implement AI-specific security guardrails, including prompt/input filtering, context validation, output sanitization, policy enforcement, and access restrictions. Embed security into the AI/ML development lifecycle by integrating controls into CI/CD and ML pipelines. Write, review, and implement code to support AI security controls, automation, integrations, and remediation activities within standard software development workflows. Support secure management of artifacts, packages, containers, and model-related assets through repositories and platforms such as JFrog Artifactory. Develop AI-focused incident response guidance and playbooks and support investigations with architectural context and mitigation recommendations. Establish processes for identifying, assessing, prioritizing, and tracking vulnerabilities or control gaps in AI-enabled applications and model-serving endpoints. Qualifications / Requirements / Skills 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, or related AI-enabled architectures. Strong background in application security principles including secure design, 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 (e.g., Azure AI Foundry, Azure OpenAI/OpenAI APIs, LangChain, Semantic Kernel, Hugging Face, TensorFlow, PyTorch). Hands-on experience implementing security controls for AI use cases (prompt filtering, output validation, model access controls, data protections, agent guardrails, monitoring). Strong understanding of AI-specific threats (prompt injection, jailbreaks, data poisoning, model inversion, data leakage). Ability to write, review, and implement code and automate security checks within applications and CI/CD pipelines. Proficiency in languages such as Python, JavaScript/TypeScript, Go, or Bash; Python preferred; comfortable with existing codebases and automation. Experience with cloud-native platforms and services (Azure preferred; AWS/GCP valuable), including APIs, containers, IAM, secrets management, logging, and pipelines. Familiarity with AI and AppSec frameworks (OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS) and secure architecture principles for AI systems. Experience with GitHub-based workflows, repository hygiene, and CI/CD integration. Knowledge of artifact, package, and binary repository management (e.g., JFrog Artifactory). Strong communication skills to collaborate with engineering, architecture, data science, security, risk, and leadership stakeholders. Education Bachelor’s degree in Computer Science, Cybersecurity, Information Security, Software Engineering, Data Science, or related field; or equivalent practical experience. Master’s degree preferred, especially in security, AI/ML, software engineering, or systems architecture. Equivalent combination of education and experience will be considered in lieu of formal advanced degrees. Nice-to-Haves Experience securing agentic AI systems, tool-calling architectures, or autonomous workflows with scoped permissions and human-approval gates. Experience with RAG security, vector databases, and context isolation. Hands-on experience red-teaming AI systems for jailbreaks or prompt injection. Experience building internal security tooling, validation harnesses, or policy enforcement layers for AI-enabled applications. Familiarity with MLOps/MSecOps platforms, model registries, feature stores, and secure model lifecycle management. Experience with enterprise AI governance or responsible AI control frameworks. Certifications or equivalent experience in cloud security, application security, or AI/ML security. Experience implementing or reviewing GitHub Actions and security checks in CI/CD. Experience with JFrog Artifactory/Xray or similar tooling for artifact management. Experience contributing to shared codebases or developer security integrations in enterprise software environments. Experience securing software supply chain components, including repositories and build provenance. What makes this role unique This role sits at the intersection of Application Security, AI/ML architecture, and hands-on security engineering. It bridges secure design with practical AI security implementation to shape secure AI adoption across the organization. Weekly Hours / Location / Salary Weekly Hours: 40 Location: Alpharetta, GA; Atlanta, GA; Bedminster, NJ; Bothell, WA; Dallas, TX; Middletown, NJ; USA: NC: Charlotte/Research Dr Salary Range: $141,300.00 - $237,400.00 EEO / Benefits It is the policy of AT&T to provide equal employment opportunity to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, gender, sexual orientation, gender identity, or any other characteristic protected by law. AT&T will provide reasonable accommodations and is a fair chance employer. Benefits include Medical/Dental/Vision, 401(k), Tuition reimbursement, Paid Time Off and Holidays, Paid Parental Leave, Caregiver Leave, and other wellness and employee programs.
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Bedminster, New Jersey, United States

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English

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