AI Security Engineer

GuidePoint Security

. Advise on and assess the security posture of AI/ML systems, including LLMs, GenAI pipelines, and model-serving infrastructure .

Posted 10/6/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in assessing and advising on the security posture of AI/ML systems, with a strong focus on threat modeling, vulnerability identification, and secure integration of AI services. Proficient in implementing security principles and controls for generative AI solutions while effectively communicating complex concepts to diverse stakeholders.

Highest-signal resume keywords

Security EngineeringCloud SecurityThreat ModelingGenerative AI SecurityAI/ML Principles

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Hard Skills

Python ProgrammingVulnerability AssessmentData ProtectionAccess ControlsThreat Modeling TechniquesGenerative AI ConceptsModel InversionData PoisoningSupply Chain SecurityAgentic Tool Usage

Soft Skills

Strong Communication SkillsInterpersonal Skills

Tools & Technologies

AWS BedrockAWS SageMakerAzure AI FoundryGoogle VertexClaude CodeOpen CodeCursorCodexAWS CloudFormationTerraform

Certifications & Qualifications

AWS Certified AI PractitionerAWS Certified Machine Learning EngineerAzure AI Engineer AssociateClaude Certified Architect

Industry Keywords

AI SecurityLLMsAdversarial InputsPrompt InjectionModel Extraction AttacksRAG ArchitecturesVector DatabasesRed TeamingMCP Client/Server ArchitectureAI Security Guidelines

Tech Stack

Tools & technologies

AWSAzureCloudPythonTerraform

About the role

Key responsibilities & impact

Advise on and assess the security posture of AI/ML systems, including LLMs, GenAI pipelines, and model-serving infrastructure
Identify vulnerabilities, attack surfaces, and gaps against frameworks such as OWASP LLM Top 10 and MITRE ATLAS
Lead threat modeling 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 such as OpenAI, Azure OpenAI, and Bedrock
Evaluate and recommend controls for data ingestion pipelines, RAG architectures, and vector databases
Serve as a security advisor to business stakeholders, AI/ML engineers, IT operations, and information security teams
Track emerging AI security research, adversarial techniques, regulatory developments, and vendor security advisories
Produce and maintain security architecture documentation, risk assessments, control frameworks, and AI security guidelines
Contribute to AI security strategies, remediation roadmaps, maturity assessments, and investment recommendations
Develop and deliver training on AI-specific risks, responsible AI usage, and secure development practices
Collaborate with peers in AppSec, Cloud Security, Vulnerability Management, and Identity and Access Management
Assist customers with the design, implementation, security, and operational management of generative AI security solutions

Requirements

What you’ll need

5+ years of experience in security engineering with a significant focus on cloud security and/or AppSec
Hands-on experience implementing, managing, securing, and supporting Agentic AI solutions within an enterprise context
Familiarity with AWS Bedrock, AWS SageMaker, Azure AI Foundry, or Google Vertex
Proficiency in at least one relevant programming language, preferably Python
Solid understanding of generative AI concepts, LLMs, context engineering, agentic tool usage, and foundational AI/ML principles
Deep knowledge and real operational experience using agentic coding assistants such as Claude Code, Open Code, Cursor, or Codex
Strong written and oral communication and interpersonal skills, with the ability to explain complex technical concepts to technical and non-technical audiences
Demonstrated experience applying security principles to AI implementations, including data protection, access controls, and threat modeling
Understanding of AI-specific security challenges including prompt injection, data poisoning, supply chain security, and model extraction attacks
Ability to embrace emerging technologies, including AI tools
Up to 10% travel
Sedentary work capability
Ability to perform substantial wrist, hand, and/or finger movement for at least 8 hours per day
Close visual acuity for computer terminal viewing and/or extensive reading for at least 8 hours per day
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
Preferred: Familiarity with red teaming of agentic systems
Preferred: Experience with Cedar, Rego, AWS CloudFormation, Terraform, OpenTofu, or equivalent technologies
Preferred: Experience designing and implementing agentic AI architectures
Preferred: Familiarity with MCP client/server architecture and associated security risks

Benefits

Comp & perks

Remote workforce primarily (U.S. based only)
Group Medical Insurance options: Zero Deductible PPO Plan with GuidePoint paying 90% of employee premiums and 70% of family premiums
High Deductible Health Plan with HSA, with GuidePoint paying 100% of employee premiums and 75% of family premiums
HSA contributions of $850 annually per employee or $1,750 annually per family plan
Group Dental Insurance with GuidePoint paying 100% of employee premiums and 75% of family premiums
12 corporate holidays
Flexible Time Off (FTO) program
Healthy mobile phone allowance
Home internet allowance
Eligibility for retirement plan after 2 months at open enrollment
Pet Benefit Option
Collaboration, mentorship, and guidance from knowledgeable colleagues