•Experience: 3+ years in security research, offensive security, ML/AI security, detection engineering, or a related discipline.
•Engineering Stack: Strong Python development skills for building research-grade tooling, async services, CLIs, SDKs, and automated reporting pipelines.
•AI & AppSec Fundamentals: Strong foundations in application security, threat modeling, and failure modes specific to autonomous and transformer-based systems.
•Attack Surface Fluency: Hands-on expertise in at least one major AI attack surface (LLM apps, agentic/tool-using workflows, or ML training/inference pipelines).
•Security Domains: Demonstrated experience in vulnerability analysis, detection engineering, adversarial ML, policy-as-code/authorization, red-teaming, or secure SDLC practices.
•GenAI / LLM Architecture: Solid grasp of LLMs, retrieval-augmented generation (RAG), prompt injection, and tool calling.
•Research Judgment: Ability to design experiments with held-out evaluations and statistically rigorous metrics; ability to distinguish exploitable vulnerabilities from theoretical edge cases, eliminate evaluation test leakage, and verify genuine generalization.
•CVE & Literature Translation: Capacity to evaluate cutting-edge security literature, advisories, and CVE disclosures and convert them into operational exploits and defensive mechanisms.
•Tooling Familiarity: Working familiarity with representative tools across categories, including:
•Languages & ML: Python, PyTorch/TensorFlow, Hugging Face Transformers.
•Agent Frameworks: LangChain, LangGraph, LlamaIndex, MCP, OpenAI/Anthropic SDKs.
•AI Red-Teaming & Guardrails: Garak, PyRIT, promptfoo, Giskard, ART, NeMo Guardrails, Guardrails AI, LLM Guard, Rebuff.
•AppSec & Scanners: Burp Suite, OWASP ZAP, Semgrep, Nuclei, ModelScan, picklescan.
•Policy & Observability: OPA/Cedar, Docker, Kubernetes, Terraform, cloud providers (AWS/Azure/GCP), and tracing tools (OpenTelemetry, LangSmith, Langfuse).
