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AI Security SME

malomatia
Doha, QAT
Full-time
Senior · 8+ years experience
Onsite
Discovered Today
awsazuregcpkuberneteslangchainpython

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Qualifications

  • Required Qualifications & Experience:
  • 8–12+ years in Cybersecurity, with 3–5+ years focused on AI/ML or data platforms (can be overlapping).
  • Hands-on experience with:
  • Cloud platforms (Azure, AWS, GCP) and their AI services.
  • At least one Agentic/GenAI stack (e.g., Transformers, LangChain/LlamaIndex, vector DBs, model gateways, MLOps platforms).
  • Proven track record designing or reviewing secure architectures for:
  • ML pipelines, LLM/RAG systems, or agentic/automation platforms.
  • Strong understanding of:
  • Cryptography, identity & access management, network & app security.
  • Data protection & privacy (PII, PHI, DPIA concepts).
  • Experience working with or mapping to frameworks/standards such as:
  • NIST AI RMF, ISO/IEC 27001, ISO/IEC 42001, SOC 2, OWASP Top 10 & OWASP GenAI/LLM Top 10, MITRE ATT&CK/ATLAS, CSA guidance.
  • Excellent communication skills: able to translate complex AI risks into clear business and technical requirements.
  • Core Technical & Domain Skills:
  • AI/ML & GenAI fundamentals:
  • Model types (LLMs, encoders, diffusion, classical ML), training/inference flows.
  • Data pipelines, feature stores, embeddings, vector databases.
  • AI Penetration testing:
  • Prompt injection, model tampering, data poisoning, output manipulation, exfiltration, shadow AI, insecure plugins/integrations.
  • Platform & Infra:
  • Kubernetes, containers, API gateways, secrets management, zero trust.
  • Observability:
  • Logging, tracing, safety & quality metrics for AI systems.
  • Health and latency monitoring.
  • Strong scripting/automation (Python preferred) for security tooling and assessments.
  • Certifications & Training (Required / Highly Desirable):
  • Essential:
  • One or more core security certifications:
  • CISSP, CISM, CCSP, ISO 27001 Lead Implementer/Lead Auditor.
  • One or more cloud security certifications:
  • CCSK (Cloud Security Alliance), AWS Security Specialty, Azure Security Engineer, Google Professional Cloud Security Engineer.
  • AI & AI Security–specific training / certs (or commitment to obtain within 6–12 months):
  • NIST AI RMF or ISO/IEC 42001-focused training.
  • Certified AI Security Professional (CAISP / similar offerings that cover LLM/GenAI threats, MITRE ATLAS, OWASP LLM Top 10).
  • OffSec / similar LLM & AI Red Teaming or GenAI security courses.
  • Vendor AI/ML certifications (Azure AI Engineer, AWS Machine Learning Specialty, GCP ML Engineer) with demonstrated security emphasis.
  • Additional desirable:
  • GIAC/GWAPT/GXPN/GCLD or similar offensive / cloud / Appsec certs.
  • Formal training in:
  • Secure MLOps & ML supply-chain security.
  • Privacy engineering & data protection (e.g., CIPT, CDPSE).
  • OWASP GenAI / LLM Top 10, MITRE ATLAS, CSA MAESTRO & other AI risk frameworks.

Responsibilities

  • AI Security Architecture
  • Design and review secure architectures for ML/LLM workloads (training, fine tuning, inference, RAG, agents, plugins, tool calling, APIs).
  • Define reference architectures for on-prem, hybrid, and cloud AI platforms (Azure OpenAI, AWS Bedrock, GCP Vertex, self-hosted models, etc.).
  • Threat Modeling & Risk Management
  • Perform AI-specific threat modeling (e.g., data poisoning, model theft, prompt injection, jailbreaks, supply-chain, inference attacks) using MAESTRO or similar framework.
  • Align controls with leading frameworks: NIST AI RMF, ISO/IEC 27001, ISO/IEC 27090, ISO/IEC 42001, OWASP GenAI / LLM Top 10, CSA & MITRE ATLAS.
  • Security Control Design & Implementation
  • Define and oversee implementation of controls for:
  • Training & data pipelines (data quality, provenance, labeling, PII protection).
  • Model & artifact integrity (signing, SBOM, secure registries).
  • Access control, isolation, rate limiting, and abuse detection.
  • Secure prompt engineering and guardrail policies.
  • Security prompt monitoring to detect ongoing attacks.
  • Data security: data classification, data masking and DLP.
  • MCP server hardening.
  • Reliability & Trustworthiness
  • Partner with engineering and data science to embed robustness, observability, fallback strategies, and evaluation pipelines (safety, bias, toxicity, hallucination monitoring).
  • Contribute to SLOs/SLAs for AI systems, including security and reliability KPIs.
  • Secure SDLC for AI
  • Embed AI security into CI/CD: scanning, dependency checks, policy-as code, red-teaming AI components pre- and post-release.
  • Incident Response & Red Teaming
  • LLM bias detection.
  • Perform red teaming activities to abuse and force AI hallucinations.
  • Develop and maintain AI-specific playbooks (prompt abuse, model exfiltration, data leakage, compromised agents).
  • Lead or support AI red/blue/purple teaming exercises using frameworks like MITRE ATLAS.
  • Governance, Compliance & Policy
  • Advice on alignment with emerging AI regulations and standards (e.g., EU AI Act, regional laws, internal AI use policies).
  • Define internal policies on responsible AI, data usage, model lifecycle, and 3rd-party AI risk management.
  • Stakeholder Leadership
  • Run workshops, training, and awareness for engineering, security, and business teams.

Preferred Qualifications

  • Strong scripting/automation (Python preferred) for security tooling and assessments.

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