Security Engineer

Precision Technologies; Irving (TX); On-site; USD 120,000 - 160,000; Part time

Job summary

Precision Technologies seeks an experienced Security Engineer - Artificial Intelligence in Irving, TX. The role focuses on securing AI/ML lifecycles, data, and infrastructure, with hands-on threat modeling, guardrails for prompts, and secure pipelines.

Onsite presence three days a week is required, and the contractor role emphasizes governance, incident response, and collaboration with product and data teams.

Qualifications

• 8 years of information security experience, with 5+ years in security engineering, app security, cloud security, or detection engineering if no degree.
• At least 3 years of AI security experience.
• Experience securing LLM-based apps, RAG systems, or agentic workflows.
• Familiarity with adversarial ML concepts like prompt injection, model inversion, and model extraction.
• Experience with one or more cloud platforms: AWS, Azure, or GCP.
• Hands-on coding in Python or similar language.
• Strong understanding of AI/LLMs, API security, identity, secrets management, and cloud controls.
• Experience embedding security controls into CI/CD pipelines.
• Proven ability to lead cross-functional security work with engineering and product teams.
• Excellent communication to technical and non-technical audiences and leadership.

Responsibilities

• Secure the AI and ML lifecycle across development, training, deployment, and monitoring.
• Define security requirements for model development, training, evaluation, and deployment.
• Threat model AI systems, 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.

Skills

Security engineering

AI Security

Cloud security

Python

CI/CD security

Cross-functional leadership

Education

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

Master’s degree in Cybersecurity, AI, CS, or related

Tools

AWS

Azure

GCP

Kubernetes

Job description

Security Engineer - Artificial Intelligence

Location: Irving, TX - 3days Onsite

12 Months/W2 Contract

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 only

Tools and Technologies You Might Use

• Cloud: AWS, Azure, GCP
• DevSecOps: GitHub Actions, GitLab CI, Azure DevOps, Terraform
• 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

Required Qualifications

• 8 years of experience in Information Security, but 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, such as 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, 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 accelerate with proactive, clear, data- driven insight, highlighting risks, roadblocks, and solutions
• Proven leadership capabilities with the ability to influence and drive change

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 (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.