{"schemaVersion":"jobsearcher.job.v1","id":"4b06b7e1649e5cc4aa66f700","url":"https://jobsearcher.com/jobs/4b06b7e1649e5cc4aa66f700","canonicalUrl":"https://jobsearcher.com/jobs/4b06b7e1649e5cc4aa66f700","title":"Senior Security Engineer, Artificial Intelligence","description":"SRI Resources (a 24 Seven Company) has an immediate, contract job opportunity for a Senior Security Engineer, Artificial Intelligence with a major corporation, on-site in Irving/Dallas, TX.\n\n12+ month contract\n\nRole Summary:\nYou 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.\n\nKey Responsibilities:\nSecure the AI and ML lifecycle\n\nDefine security requirements for model development, training, evaluation, deployment, and monitoring\nThreat model AI systems and AI features in products, including abuse cases and misuse scenarios\nEstablish secure-by-design patterns for model endpoints, prompts, RAG pipelines, and agent workflows\nValidate controls for model access, rate limiting, tenant isolation, and secrets management\n\nProtect data used by AI\nClassify and control training data, fine-tuning data, prompts, and retrieved context\nImplement guardrails for sensitive data exposure, including PII and PCI data\nDefine retention, deletion, and lineage requirements for AI datasets and outputs\nPartner with Privacy and Legal on data handling, regulatory expectations, and third-party data use\n\nSecure AI infrastructure and supply chain\nHarden AI platforms, GPU and container workloads, model registries, and artifact stores\nAssess risks in third-party models, libraries, embeddings, and model hosting services\nDefine integrity controls for model artifacts, evaluation sets, and pipeline automation\nBuild CI and CD checks for AI assets, including scanning, signing, and policy enforcement\n\nDetection, monitoring, and response for AI threats\nBuild logging standards for model requests, responses, tool calls, and retrieval events\nCreate detections for prompt injection, data exfiltration attempts, model extraction signals, and anomalous usage\nDevelop incident response playbooks for AI events, including containment and rollback plans\nRun security testing for AI features, including red teaming and structured adversarial testing\n\nGovernance and program delivery\nCreate practical AI security standards, patterns, and reference architectures\nDefine KPIs such as reduction in sensitive output leakage, time to detect misuse, and policy coverage\nLead risk reviews for new AI features and vendor assessments for AI services\nTrain engineering and data science teams on secure AI patterns and common attack paths\n\nTools and Technologies You Might Use:\nCloud: AWS, Azure, GCP\nContainers: Kubernetes, Docker\nDevSecOps: GitHub Actions, GitLab CI, Azure DevOps, Terraform\nSecurity: SIEM, EDR, WAF, API gateways, secrets managers\nAI stack: model gateways, vector databases, model registries, ML pipelines\n\nExamples of Work and Technical Scope:\nSecure an LLM gateway with authentication, authorization, quotas, content filtering, and audit logging\nAdd prompt injection defenses for an agent that uses tools like web search and internal APIs\nImplement retrieval filtering, context redaction, and output scanning for a RAG application\nBuild model artifact signing and verification into the release pipeline\nCreate detections in SIEM for abnormal model usage, including model scraping patterns\n\nRequired Qualifications:\n8 years of experience in Information Security, but 5+ years in security engineering, application security, cloud security, or detection engineering if there is no degree\nAt least 3 years of experience in AI Security\nExperience securing LLM-based applications, RAG systems, or agentic workflows\nFamiliarity with adversarial ML concepts, such as prompt injection, model inversion, and model extraction\nExperience with one or more cloud platforms, AWS, Azure, or GCP\nHands-on experience with at least one programming language, Python preferred\nStrong understanding of AI, LLMs, API security, identity, secrets management, and cloud controls\nExperience building security controls into CI and CD pipelines\nProven ability to lead cross-functional security work with engineering and product teams\nEffectively communicate complex technical concepts to both technical and non-technical stakeholders\nEffectively communicate to leadership and know when to escalate with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions\nProven leadership capabilities with the ability to influence and drive change\n\nPreferred Qualifications:\nBachelor's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field\nExperience with Kubernetes and container security\nMaster's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field\nAI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy)\nExperience with security telemetry and detections in SIEM or EDR platforms\n\nAbout SRI Resources\nSRI Resources, founded more than 3 decades ago, is a trusted staffing partner specializing in Technology (IT, Creative, & Marketing), Finance, & Accounting placements. We work with companies nationwide that need skilled professionals and connect them with candidates like you looking for the right opportunity. We are always looking for professionals at all experience levels and in various specialties to meet our clients' hiring needs. As a member of our team, you will have access to benefits including medical, dental, and vision coverage, a 401(k) with company match, short-term disability, life insurance with AD&D. If this position fits interests you, we encourage you to apply. You can view all our job openings at SRI Resources Careers.","company":"Software Resources","rawCompany":"software resources","city":"Arlington","state":"TX","isRemote":false,"isActive":false,"createdAt":"2026-11-13T10:09:36.319Z","occupations":[{"code":"15-1299.05","title":"Information Security Engineers","slug":"information-security-engineers"},{"code":"15-1212.00","title":"Information Security Analysts","slug":"information-security-analysts"},{"code":"13-1199.07","title":"Security Management Specialists","slug":"security-management-specialists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541519","title":"Other Computer Related Services","slug":"other-computer-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Security Engineer, Artificial Intelligence","description":"SRI Resources (a 24 Seven Company) has an immediate, contract job opportunity for a Senior Security Engineer, Artificial Intelligence with a major corporation, on-site in Irving/Dallas, TX.\n\n12+ month contract\n\nRole Summary:\nYou 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.\n\nKey Responsibilities:\nSecure the AI and ML lifecycle\n\nDefine security requirements for model development, training, evaluation, deployment, and monitoring\nThreat model AI systems and AI features in products, including abuse cases and misuse scenarios\nEstablish secure-by-design patterns for model endpoints, prompts, RAG pipelines, and agent workflows\nValidate controls for model access, rate limiting, tenant isolation, and secrets management\n\nProtect data used by AI\nClassify and control training data, fine-tuning data, prompts, and retrieved context\nImplement guardrails for sensitive data exposure, including PII and PCI data\nDefine retention, deletion, and lineage requirements for AI datasets and outputs\nPartner with Privacy and Legal on data handling, regulatory expectations, and third-party data use\n\nSecure AI infrastructure and supply chain\nHarden AI platforms, GPU and container workloads, model registries, and artifact stores\nAssess risks in third-party models, libraries, embeddings, and model hosting services\nDefine integrity controls for model artifacts, evaluation sets, and pipeline automation\nBuild CI and CD checks for AI assets, including scanning, signing, and policy enforcement\n\nDetection, monitoring, and response for AI threats\nBuild logging standards for model requests, responses, tool calls, and retrieval events\nCreate detections for prompt injection, data exfiltration attempts, model extraction signals, and anomalous usage\nDevelop incident response playbooks for AI events, including containment and rollback plans\nRun security testing for AI features, including red teaming and structured adversarial testing\n\nGovernance and program delivery\nCreate practical AI security standards, patterns, and reference architectures\nDefine KPIs such as reduction in sensitive output leakage, time to detect misuse, and policy coverage\nLead risk reviews for new AI features and vendor assessments for AI services\nTrain engineering and data science teams on secure AI patterns and common attack paths\n\nTools and Technologies You Might Use:\nCloud: AWS, Azure, GCP\nContainers: Kubernetes, Docker\nDevSecOps: GitHub Actions, GitLab CI, Azure DevOps, Terraform\nSecurity: SIEM, EDR, WAF, API gateways, secrets managers\nAI stack: model gateways, vector databases, model registries, ML pipelines\n\nExamples of Work and Technical Scope:\nSecure an LLM gateway with authentication, authorization, quotas, content filtering, and audit logging\nAdd prompt injection defenses for an agent that uses tools like web search and internal APIs\nImplement retrieval filtering, context redaction, and output scanning for a RAG application\nBuild model artifact signing and verification into the release pipeline\nCreate detections in SIEM for abnormal model usage, including model scraping patterns\n\nRequired Qualifications:\n8 years of experience in Information Security, but 5+ years in security engineering, application security, cloud security, or detection engineering if there is no degree\nAt least 3 years of experience in AI Security\nExperience securing LLM-based applications, RAG systems, or agentic workflows\nFamiliarity with adversarial ML concepts, such as prompt injection, model inversion, and model extraction\nExperience with one or more cloud platforms, AWS, Azure, or GCP\nHands-on experience with at least one programming language, Python preferred\nStrong understanding of AI, LLMs, API security, identity, secrets management, and cloud controls\nExperience building security controls into CI and CD pipelines\nProven ability to lead cross-functional security work with engineering and product teams\nEffectively communicate complex technical concepts to both technical and non-technical stakeholders\nEffectively communicate to leadership and know when to escalate with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions\nProven leadership capabilities with the ability to influence and drive change\n\nPreferred Qualifications:\nBachelor's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field\nExperience with Kubernetes and container security\nMaster's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field\nAI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning Engineer, ISC2 Building AI Strategy)\nExperience with security telemetry and detections in SIEM or EDR platforms\n\nAbout SRI Resources\nSRI Resources, founded more than 3 decades ago, is a trusted staffing partner specializing in Technology (IT, Creative, & Marketing), Finance, & Accounting placements. We work with companies nationwide that need skilled professionals and connect them with candidates like you looking for the right opportunity. We are always looking for professionals at all experience levels and in various specialties to meet our clients' hiring needs. As a member of our team, you will have access to benefits including medical, dental, and vision coverage, a 401(k) with company match, short-term disability, life insurance with AD&D. If this position fits interests you, we encourage you to apply. You can view all our job openings at SRI Resources Careers.","datePosted":"2026-11-13T10:09:36.319Z","dateModified":"2026-11-13T10:09:36.319Z","hiringOrganization":{"@type":"Organization","name":"Software Resources","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Arlington","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"4b06b7e1649e5cc4aa66f700"},"url":"https://jobsearcher.com/jobs/4b06b7e1649e5cc4aa66f700"}}