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AI Security Researcher
2 weeks ago
Position Overview:
We are seeking an AI Security Researcher with a deep understanding of the AI technology stack and a proactive, adversarial mindset. In this role, you will be responsible for identifying and mitigating security vulnerabilities across our entire AI infrastructure, from the underlying models to the agentic systems and client-side hosts. You will be instrumental in shaping our AI security posture by analyzing novel threats, stress-testing our systems, and ensuring the secure configuration and deployment of our AI solutions.
The ideal candidate will have hands-on experience with the entire AI lifecycle, including LLM setup and the emerging protocols that govern AI interactions.
Key Responsibilities:
● LLM & AI System Hardening: Ability to setup, configure, and deploy Large Language Models (LLMs) in cloud environments. This includes securing the full lifecycle, from training data pipelines to inference endpoints and the AI client hosts interacting with the models. Ability to easily operate in AWS and other cloud infrastructure environments.
● Vulnerability Assessment & Mitigation: Discover and document vulnerabilities in AI models and systems. Validate known vulnerabilities against current AI models and
systems. Collaborate with engineering and product teams to design and implement effective and scalable mitigations.
● Threat Modeling & Research: Conduct threat model assessments against AI infrastructure components to identify gaps and effective mitigation strategies. Contribute to the broader security community through publications and presentations.
● Protocol & Infrastructure Analysis: Investigate and perform gap analysis on the communication channels within the AI ecosystem. This includes deep analysis of the Model Context Protocol (MCP) to secure data flow between MCP hosts, clients, and servers. Analyze agentic and client AI hosts for security gaps and hardening measures.
● Adversarial Research & Red Teaming: Proactively identify, analyze, and exploit potential security gaps in AI/ML systems, including LLMs, agentic AI frameworks, and their underlying infrastructure. Simulate real-world attacks to test the resilience mechanisms against known techniques and investigate novel attack methods.
Required Qualifications & Skills:
● Bachelor's degree in Computer Science, Cybersecurity, or a related field, or 5+ years equivalent practical experience.
● Proven experience in a security-focused role, such as security research, penetration testing, or red teaming.
● Demonstrable experience with the setup, configuration, and security of Large Language Models (LLMs) and the associated MLOps lifecycle.
● Understanding of the architecture and security implications of **Agentic AI** systems and frameworks (e.g., LangChain, AutoGPT).
● Familiarity with the **Model Context Protocol (MCP)**, including its core components (Hosts, Clients, Servers) and transport mechanisms (e.g., stdio, HTTP/SSE).
● Knowledge of network and infrastructure protocols relevant to AI systems (e.g., TLS/SSL, REST APIs, AUTH, gRPC) and cloud security principles.
● A strong adversarial mindset with the ability to think creatively to uncover novel attack vectors against AI systems.
● Proficiency in scripting languages like Python or Bash for security testing and automation.
Preferred Qualifications:
● Master's or Ph.D. in a relevant field with a focus on AI/ML security.
● Experience participating in or leading AI red teaming exercises.
● Knowledge of AI security frameworks and benchmarks (e.g., MITRE ATT&CK for ML, OWASP Top 10 for LLMs).
● Experience securing AI systems in a cloud environment (AWS, GCP, Azure).
● Contributions to the open-source community, security publications, or conference talks
Interested candidates can share their resume at and
