AI Security Engineer / Researcher

Overview

Intellectsoft is building a dedicated research capability at the intersection of cybersecurity and artificial intelligence. The role owns end-to-end AI security research across the modern AI stack, turning open security challenges into adversarially testable hypotheses and developing exploits and defensive controls that generalize across models and enterprise environments.

What you'll do

• Own end-to-end AI security research problems across generative and agentic systems, LLM copilots, machine learning models, training/data pipelines, and enterprise AI governance.
• Turn open security challenges into adversarially testable hypotheses.
• Develop exploits and defensive controls that generalize across models and enterprise environments.

What you'll need

• 3+ years in security research, offensive security, ML/AI security, detection engineering, or a related discipline.
• Strong Python development skills for building research-grade tooling, async services, CLIs, SDKs, and automated reporting pipelines.
• Strong foundations in application security, threat modeling, and failure modes specific to autonomous and transformer-based systems.
• Hands-on expertise in at least one major AI attack surface (LLM apps, agentic/tool-using workflows, or ML training/inference pipelines).
• Demonstrated experience in vulnerability analysis, detection engineering, adversarial ML, policy-as-code/authorization, red-teaming, or secure SDLC practices.
• Solid grasp of LLMs, retrieval-augmented generation (RAG), prompt injection, and tool calling.
• 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.
• Capacity to evaluate cutting-edge security literature, advisories, and CVE disclosures and convert them into operational exploits and defensive mechanisms.
• Working familiarity with representative tools across categories, including: Python, PyTorch/TensorFlow, Hugging Face Transformers; LangChain, LangGraph, LlamaIndex, MCP, OpenAI/Anthropic SDKs; Garak, PyRIT, promptfoo, Giskard, ART, NeMo Guardrails, Guardrails AI, LLM Guard, Rebuff; Burp Suite, OWASP ZAP, Semgrep, Nuclei, ModelScan, picklescan; OPA/Cedar, Docker, Kubernetes, Terraform, cloud providers (AWS/Azure/GCP), and tracing tools (OpenTelemetry, LangSmith, Langfuse).

Nice to have

• Dual experience spanning both offensive red-teaming and defensive engineering (detection, policy enforcement, governance).
• Practical experience working with agentic architectures, MCP ecosystems, and multi-agent frameworks.
• Applied knowledge of AI governance and risk frameworks (NIST AI RMF, ISO 42001, EU AI Act, MITRE ATLAS, OWASP LLM & ASI Top 10).
• Experience implementing policy-as-code (Cedar, OPA), applied cryptography (digital signatures, hash-chained audit logs), or CI/CD security quality gates.
• Proven track record in cross-framework, cross-dataset, or real-world validation of security controls.
• Published security research, CVE disclosures, security advisories, or competitive CTF / red-teaming accomplishments.

Details

• Locations: Bengaluru, Karnataka, India; Pune, Maharashtra, India.
• Employment-based cooperation.
• Flexible hours and work setup.