AI Security SME

Job Description You will lead the security architecture, governance, and assurance of our AI and GenAI platforms across the organization. This includes classical ML systems and modern LLM/agentic architectures. You will define how models are designed, deployed, monitored, and defended; ensure they are robust, explainable, privacy-preserving, and compliant; and act as the go-to expert for AI security across products, data, cloud, and security teams. 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. 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. Essential Certifications & Training (Required / Highly Desirable): • 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. About Malomatia ABOUT US malomatia is a leading Qatar-based IT services and solutions provider, bringing together top Qatari and international talent to deliver innovative, end-to-end technology solutions that empower clients to achieve their strategic goals. Our mission Empowering Qatar’s businesses and governments to leap into the digital future with agile, knowledge-driven solutions. Our vision To become Qatar’s trusted knowledge partner in digital transformation, disrupting industries, shaping the future, and building a world-class tech ecosystem. Driving change that makes a real impact Since 2008, malomatia has been driving Qatar’s digital transformation through innovative, ISO-certified IT solutions. With expertise across key public and private sectors, we empower the nation’s vision with advanced services in cloud, cybersecurity, AI, and contact center excellence, elevating the role of technology in shaping Qatar’s sustainable future. About The Team Established in 2008, malomatia is a Qatari leader in IT services and digital transformation. We serve key sectors including Government, Healthcare, Education, Customs, and Transportation, delivering impactful solutions that support national development goals. Powered by a diverse team of skilled Qatari and internatio

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.

AI Security Architecture; Threat Modeling & Risk Management; Security Control Design & Implementation; Reliability & Trustworthiness; Secure SDLC for AI; Incident Response & Red Teaming; Governance, Compliance & Policy; Stakeholder Leadership. Includes designing secure architectures for ML/LLM workloads, defining reference architectures for on-prem, hybrid, and cloud AI platforms; performing AI-specific threat modeling; defining and overseeing controls for data pipelines, model integrity, access control, secure prompt engineering, data security, MCP server hardening; embedding AI security into CI/CD; conducting red/blue/purple teaming; developing AI-specific playbooks; aligning with AI regulations and internal policies; running workshops and training.