Remote AI Security Lead Engineer

1114 Target Enterprise Inc; Brooklyn Park (MN); Hybrid; USD 132,000 - 238,000; Full time

Benefits offered by this job

Remote or Hybrid work

Job summary

Target is seeking a Lead Engineer on the AI Security team to assess and strengthen the security of AI systems across the enterprise. You will identify risks, review architectures, and drive security controls implementation in cloud and hybrid environments.

The role requires 7+ years in tech, deep AI security knowledge, experience in GCP/AWS/Azure, and hands-on skills with containerization, threat modeling, and secure software practices.

Qualifications

• 4-year degree or equivalent experience.
• Software developer comfortable in several programming languages across different platforms.
• 7+ years of hands-on experience in technology, with deep knowledge of multiple cybersecurity domains including Information Protection, Cloud Security (GCP strongly preferred), Networking Security, IAM, Automation, and SIEM
• LLM Security expertise (RAG, MCP, input validation, sandboxing etc.)
• In-depth understanding of OWASP Top 10 for Large Language Model Applications
• Solid understanding of AI and ML
• Understanding of prompt injection and its various styles (direct, indirect, RAG poisoning, etc.)
• Knowledge of MCP authentication patterns including dynamic client registration
• Knowledge of RAG authorization patterns
• Experience mitigating the security risks of local coding agents
• Solid understanding of containerization technologies and tools

Responsibilities

• Collaborate with AI platform, product, and engineering teams to evaluate the security posture of AI systems throughout their lifecycle.
• Define and evaluate security assessment methodologies, testing frameworks, and assurance standards for AI technologies.
• Develop and execute security validation strategies for AI systems, including threat modeling, attack simulation, and adversarial testing.
• Conduct deep technical reviews of AI products, platforms, and architectures to identify opportunities for security improvement.
• Partner with engineering teams to validate secure deployment patterns for AI workloads across cloud and hybrid environments.
• Assess AI architectures, models, agents, and supporting infrastructure to identify security risks, vulnerabilities, and design weaknesses.
• Evaluate the effectiveness of existing security controls, guardrails, and mitigations protecting AI applications and platforms.
• Perform hands-on security analysis and testing of complex AI-enabled systems, identifying gaps in architecture, implementation, and operational controls.
• Recommend risk mitigation strategies and prioritized remediation plans to improve the security and resilience of AI systems.
• Provide expert guidance on AI security best practices, including model security, prompt injection defenses, agent security, supply chain security, and data protection.
• Prioritize high-impact security improvements that measurably reduce risk while enabling innovation and business objectives.
• Identify emerging AI-specific threats, attack techniques, and vulnerabilities, and communicate their potential business impact.
• Collaborate with security, architecture, and engineering teams to continuously improve AI security controls and governance practices.
• Efficiently assess and communicate security risks to stakeholders, balancing technical realities, business priorities, and organizational objectives.
• Serve as a trusted advisor on AI security, helping teams make informed decisions as AI capabilities evolve across the enterprise.

Skills

LLM Security

Threat modeling

AI/ML knowledge

Prompt injection

Security testing

Strong communication

Education

4-year degree or equivalent experience

Tools

GCP

Vertex AI

AWS

Azure

Docker

Kubernetes

SIEM

Job description

Target is seeking a Lead Engineer on the AI Security team to assess and strengthen the security of AI systems across the enterprise. You will identify risks, review architectures, and drive security controls implementation in cloud and hybrid environments.

The role requires 7+ years in tech, deep AI security knowledge, experience in GCP/AWS/Azure, and hands-on skills with containerization, threat modeling, and secure software practices.