Senior Security Engineer, Artificial Intelligence

InnoRev Technologies Inc; Irving (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

InnoRev Technologies Inc. is seeking a Senior Security Engineer for Artificial Intelligence to design, build, and run security controls across the AI lifecycle in an on-site Irving, TX role.

You will secure model development, training data, pipelines, APIs, and AI-enabled applications, partnering with product, engineering, data science, and compliance teams to mitigate model misuse, data leakage, supply chain threats, and adversarial attacks.

Qualifications

• Bachelor's degree in a highly technical field such as Cybersecurity, AI or CS.
• 5+ years in security engineering, app security, cloud security, or detection engineering.
• Experience securing LLM-based apps, RAG systems, or agentic workflows.
• Familiarity with adversarial ML concepts (prompt injection, model inversion, extraction).
• Experience with one or more cloud platforms (AWS/Azure/GCP).
• Experience with Kubernetes and container security.
• Hands-on programming, Python preferred.
• Strong understanding of AI/LLMs, API security, identity, secrets management, and cloud controls.
• Experience building security into CI/CD pipelines.
• Proven ability to lead cross-functional security work with engineering and product teams.
• Effective communication to technical and non-technical stakeholders; leadership escalation.
• Proven leadership capabilities to influence and drive change.

Responsibilities

• Secure the AI/ML lifecycle across development, training, deployment, and monitoring.
• Define security requirements for model development, training, evaluation, deployment, and monitoring.
• Threat model AI systems and features, including 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 and control training data, prompts, and context.
• Implement guardrails for sensitive data exposure (PII, PCI).
• Define retention, deletion, and lineage for AI datasets and outputs.
• Collaborate with Privacy/Legal on data handling and third-party data use.
• Harden AI infrastructure and supply chain, including GPUs, containers, and registries.
• Assess risks in third-party models, libraries, embeddings, hosting services.
• Define integrity controls for model artifacts and evaluation sets.
• Build CI/CD checks for AI assets (scanning, signing, policy enforcement).
• Detect, monitor, and respond to AI threats; create detections for prompts and data exfiltration.
• Develop incident response playbooks for AI events (containment, rollback).
• Run security testing for AI features (red team, adversarial testing).
• Governance and program delivery; create security standards and reference architectures.
• Define KPIs like leakage reduction, detection speed, and policy coverage.
• Lead risk reviews for new features and vendor AI services.

Skills

LLM security

Adversarial ML

Kubernetes security

Python

Cloud security

CI/CD security

Cross-functional collaboration

Security architecture

Threat modeling

Stakeholder communication

Education

Bachelor’s degree in Cybersecurity, AI, CS, or related field

Tools

AWS

Azure

GCP

Kubernetes

SIEM

EDR

WAF

API gateways

Secrets managers

Job description

Job Title: Senior Security Engineer, Artificial Intelligence Reports To: Sr. Manager, Security Architecture Location: Irving, TX onsite Senior Security Engineer, Artificial Intelligence. Fully onsite role in Irving. Additional info: 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. 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.

Responsibilities

• 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
• Security: SIEM, EDR, WAF, API gateways, secrets managers
• 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

Qualifications

• 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 escalatere with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions
• Proven leadership capabilities with the ability to influence and drive change

Required Skills

• Securing LLM-based applications, RAG systems, or agentic workflows
• Adversarial ML concepts, such as prompt injection, model inversion, and model extraction
• Kubernetes and container security
• Programming language, Python preferred
• AI, LLMs, API security, identity, secrets management, and cloud controls
• Building security controls into CI and CD pipelines
• Cross-functional security work with engineering and product teams
• Communication of complex technical concepts to both technical and non-technical stakeholders
• Communication to leadership and escalation