AI Security Engineer
New
G
GuidePoint SecurityAI cybersecurity
Source API remote eligibility restrictions: United StatesFull-TimeMiddle
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years of experience in security engineering with a significant focus on cloud security and/or AppSec
- Required Skills
- Python
Requirements
- Have 5+ years of experience in security engineering with a significant focus on cloud security and/or AppSec.
- Have hands-on experience implementing, managing, securing, and supporting Agentic AI solutions in an enterprise context.
- Be familiar with AI-focused services from major cloud providers, such as AWS Bedrock, AWS SageMaker, Azure AI Foundry, or Google Vertex.
- Be proficient in at least one relevant programming language; Python is preferred.
- Have a solid understanding of generative AI, LLMs, context engineering, agentic tool usage, and foundational AI/ML principles.
- Have deep knowledge and real operational experience using agentic coding assistants such as Claude Code, Open Code, Cursor, or Codex.
- Have experience applying security principles to AI implementations, including data protection, access controls, and AI threat modeling.
- Understand AI security challenges such as prompt injection, data poisoning, supply-chain security, and model extraction attacks.
- Preferred: AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer, Azure AI Engineer Associate, or Claude Certified Architect.
- Preferred: understanding or experience with model fine-tuning techniques and red teaming of agentic systems.
- Preferred: experience with policy-as-code languages such as Cedar or Rego and IaC tools such as AWS CloudFormation, Terraform, or OpenTofu.
- Preferred: experience designing and implementing agentic AI architectures that balance security and autonomy, and familiarity with MCP client/server architecture, agentic skills, and associated security risks.
Responsibilities
- Assess AI/ML systems, LLMs, GenAI pipelines, and model-serving infrastructure for vulnerabilities, attack surfaces, and gaps against industry frameworks.
- Lead threat modeling exercises for AI workloads, including adversarial inputs, prompt injection, model inversion, data poisoning, and supply-chain risks.
- Advise on secure integration of SaaS AI services and APIs, including OpenAI, Azure OpenAI, and Bedrock.
- Evaluate and recommend controls for data ingestion pipelines, RAG architectures, and vector databases.
- Advise business stakeholders, AI/ML engineers, IT operations, and information security teams on AI risk and security.
- Track AI security research, adversarial techniques, regulatory developments, and vendor security advisories.
- Produce and maintain AI security architecture documentation, risk assessments, control frameworks, and guidelines.
- Contribute to AI security strategy, remediation roadmaps, capability maturity assessments, and investment recommendations.
- Develop and deliver training on AI-specific risks, responsible AI usage, and secure development practices.
View Full Description & ApplyYou'll be redirected to the employer's site
