Senior Security Engineer, Artificial Intelligence

Design and deliver security controls for AI systems across the full lifecycle, from secure model development to detection and incident response.

Responsibilities

• Define security requirements across model development, training, evaluation, deployment, and monitoring
• Threat model AI systems and AI features, covering 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
• Classify and control training data, fine-tuning data, prompts, and retrieved context
• Implement guardrails to prevent 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
• 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
• Create logging standards for model requests, responses, tool calls, and retrieval events
• Develop 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
• 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

Requirements

• 8 years of experience in Information Security, or 5+ years in security engineering, application security, cloud security, or detection engineering if there is no degree
• At least 3 years of experience in AI Security
• Experience securing LLM-based applications, RAG systems, or agentic workflows
• Familiarity with adversarial ML concepts, including prompt injection, model inversion, and model extraction
• Experience with one or more cloud platforms: AWS, Azure, or GCP
• Hands-on experience with at least one programming language, with 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
• Communicate complex technical concepts to technical and non-technical stakeholders
• Communicate with leadership and escalate with proactive, clear, data-driven insight, highlighting risks, roadblocks, and solutions
• Proven leadership capabilities to influence and drive change

Technologies

• AWS, Azure, GCP
• Kubernetes, Docker
• GitHub Actions, GitLab CI, Azure DevOps
• Terraform
• SIEM, EDR, WAF, API gateways, secrets managers
• Python
• Model gateways, vector databases, model registries, ML pipelines
• PII, PCI

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 using 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 SIEM detections for abnormal model usage, including model scraping patterns

Preferred Qualifications

• Bachelor's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field
• Experience with Kubernetes and container security
• Master's degree in Cybersecurity, Artificial Intelligence, Computer Science, or related highly technical field
• AI/ML certifications such as 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

Benefits

• Medical, dental, and vision coverage
• 401(k) with company match
• Short-term disability
• Life insurance with AD&D

Location and Job Type

• Irving, TX (onsite)
• Contract