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7-Eleven, Inc. will consider qualified applicants with a criminal history pursuant to the California Fair Chance Act. You do not need to disclose your criminal history or participate in a background check until a condition job offer is made to you. After making a conditional offer and running a background check, if the Company is concerned about a conviction that is directly related to the job, you will be given the chance to explain circumstances surrounding the conviction, provide mitigating evidence, or challenge the accuracy of the background report. Find out more about the Fair Chance Act by visiting the Civil Rights Department’s website.

Pursuant to the San Francisco Fair Chance Ordinance and/or any other applicable law, 7-Eleven, Inc. will consider for employment qualified applicants with arrest and conviction records.

We will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Los Angeles Fair Chance Initiative For Hiring.

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Sr AI Security Engineer

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Job ID R26_2184 Address 3200 Hackberry Road, Irving, Texas, 75063, United States Location Irving, Texas

7-Eleven is an iconic family of brands with over 86,000 locations, surpassing every retailer in the world. We revolutionize convenience, restaurants and fuel through cutting edge innovation — working hard to be the customer's first choice. 7-Eleven empowers our employees to "activate awesome" and make a meaningful impact in their stores and communities every day. If you're ready to grow, lead and make a difference, come join our team and help shape the future of convenience.

Role Summary
You design, build, and run security controls for artificial intelligence systems across the full lifecycle. You secure model development, training data, pipelines, APIs, and AI-enabled applications. You work with product, engineering, data science, and compliance teams to reduce exposure from model misuse, data leakage, supply chain threats, and adversarial attacks. You deliver measurable improvements in AI governance, detection, and incident response.

Key Responsibilities

Secure the AI and ML lifecycle

  • Define security requirements for model development, training, evaluation, deployment, and monitoring
  • Threat model AI systems and AI features in products, including abuse cases and misuse scenarios
  • Establish secure-by-design patterns for model endpoints, prompts, RAG pipelines, and agent workflows
  • Validate controls for model access, rate limiting, tenant isolation, and secrets management

Protect data used by AI

  • Classify and control training data, fine-tuning data, prompts, and retrieved context
  • Implement guardrails for sensitive data exposure, including PII and PCI data
  • Define retention, deletion, and lineage requirements for AI datasets and outputs
  • Partner with Privacy and Legal on data handling, regulatory expectations, and third-party data use

Secure AI infrastructure and supply chain

  • Harden AI platforms, GPU and container workloads, model registries, and artifact stores
  • Assess risks in third-party models, libraries, embeddings, and model hosting services
  • Define integrity controls for model artifacts, evaluation sets, and pipeline automation
  • Build CI and CD checks for AI assets, including scanning, signing, and policy enforcement

Detection, monitoring, and response for AI threats

  • Build logging standards for model requests, responses, tool calls, and retrieval events
  • Create detections for prompt injection, data exfiltration attempts, model extraction signals, and anomalous usage
  • Develop incident response playbooks for AI events, including containment and rollback plans
  • Run security testing for AI features, including red teaming and structured adversarial testing

Governance and program delivery

  • Create practical AI security standards, patterns, and reference architectures
  • Define KPIs such as reduction in sensitive output leakage, time to detect misuse, and policy coverage
  • Lead risk reviews for new AI features and vendor assessments for AI services
  • Train engineering and data science teams on secure AI patterns and common attack paths

Tools and Technologies You Might Use

  • Cloud: AWS, Azure, GCP
  • Containers: Kubernetes, Docker
  • DevSecOps: GitHub Actions, GitLab CI, Azure DevOps, Terraform
  • Security: SIEM, EDR, WAF, API gateways, secrets managers
  • AI stack: model gateways, vector databases, model registries, ML pipelines

Examples of Work and Technical Scope

  • Secure an LLM gateway with authentication, authorization, quotas, content filtering, and audit logging
  • Add prompt injection defenses for an agent that uses tools like web search and internal APIs
  • Implement retrieval filtering, context redaction, and output scanning for a RAG application
  • Build model artifact signing and verification into the release pipeline
  • Create detections in SIEM for abnormal model usage, including model scraping patterns

Required Qualifications

  • Bachelor’s degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field
  • 5+ years in security engineering, application security, cloud security, or detection engineering
  • Experience securing LLM-based applications, RAG systems, or agentic workflows
  • Familiarity with adversarial ML concepts, such as prompt injection, model inversion, and model extraction
  • Experience with one or more cloud platforms, AWS, Azure, or GCP
  • Experience with Kubernetes and container security
  • Hands-on experience with at least one programming language, Python preferred
  • Strong understanding of AI, LLMs, API security, identity, secrets management, and cloud controls
  • Experience building security controls into CI and CD pipelines
  • Proven ability to lead cross-functional security work with engineering and product teams
  • Effectively communicate complex technical concepts to both technical and non-technical stakeholders
  • Effectively communicate to leadership and know when to escalate with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions
  • Proven leadership capabilities with the ability to influence and drive change

Preferred Qualifications

  • Master’s degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field
  • AI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy)
  • Experience with security telemetry and detections in SIEM or EDR platforms

If an hourly or salary range is included in this ad it represents the range 7-Eleven in good faith believes is the range of compensation for this role at the time of this posting. The Company may ultimately pay more or less than the posted range. This range is only applicable for jobs to be performed in this state. This range may be modified in the future. No amount is considered to be wages or compensation until such amount is earned, vested, and determinable under the terms and conditions of the applicable policies and plans. The amount and availability of any bonus, commission, long-term incentive compensation, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.

For a general description of all benefits 7-Eleven is offering in the US for the position, please visit this link.

For a general description of all benefits 7-Eleven is offering in Canada for the position, please visit this link.

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