Ai Security Model Cybersecurity Jobs

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Senior Security Engineer, Artificial Intelligence

SRI Resources Irving, TX

Irving, TX

Today

Contract Senior Ai Security Ai Security Model

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Job Description

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

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