LLM Penetration Test Automation Engineer
Budget: $750 – $1,500 USD
Location: Remote | Type: Contract-to-Hire | Division: SADI Inc., Unit 42
We are seeking a highly skilled AI/ML Systems Engineer with experience in secure cloud deployment and advanced prompt engineering to lead the automation of the SYLARQ Protocol — a multi-layer adversarial pen-testing framework designed to expose collapse behaviors in Large Language Models (LLMs). Your task will be to architect, implement, and deploy a scalable, cloud-native pen-testing platform that simulates the full depth of SYLARQ’s recursive and symbolic audit stack (Layers D0–D16 through Ω and ∞).
Responsibilities:
Automate 216+ structured prompts across symbolic, causal, and recursive test layers
Develop secure execution pipelines using APIs (e.g., OpenAI, Anthropic, Hugging Face)
Integrate forensic response logging, collapse detection, and anomaly scoring
Containerize and deploy platform on secure cloud infrastructure (AWS, GCP, or Azure)
Contribute to the design of AI-based collapse classification algorithms
Qualifications:
Strong Python and API automation skills
Background in LLM architecture, adversarial testing, or red teaming
Experience with container orchestration (Docker/Kubernetes)
Familiarity with CI/CD and secure logging pipelines
We are seeking a highly skilled AI/ML Systems Engineer with experience in secure cloud deployment and advanced prompt engineering to lead the automation of the SYLARQ Protocol — a multi-layer adversarial pen-testing framework designed to expose collapse behaviors in Large Language Models (LLMs). Your task will be to architect, implement, and deploy a scalable, cloud-native pen-testing platform that simulates the full depth of SYLARQ’s recursive and symbolic audit stack (Layers D0–D16 through Ω and ∞).
Responsibilities:
Automate 216+ structured prompts across symbolic, causal, and recursive test layers
Develop secure execution pipelines using APIs (e.g., OpenAI, Anthropic, Hugging Face)
Integrate forensic response logging, collapse detection, and anomaly scoring
Containerize and deploy platform on secure cloud infrastructure (AWS, GCP, or Azure)
Contribute to the design of AI-based collapse classification algorithms
Qualifications:
Strong Python and API automation skills
Background in LLM architecture, adversarial testing, or red teaming
Experience with container orchestration (Docker/Kubernetes)
Familiarity with CI/CD and secure logging pipelines