AI Security Engineer in banking domain

We have an urgent requirement for AI Security Engineer in banking domain(Prefered)is required for our banking clients in Abu Dhabi ,UAEDesign and secure Agentic AI, Generative AI, LLM, and ML solutions by implementing controls against prompt injection, data leakage, jailbreaks, unauthorized tool execution, and RAG security threats.--MustSecure AI workloads across Azure/cloud environments using Microsoft security technologies (Entra, Defender, Sentinel, Purview), while implementing IAM, secrets management, Kubernetes, Docker, Terraform, and DevSecOps best practices.--MustPerform threat modeling, security architecture reviews, adversarial testing, red teaming, vulnerability assessments, and develop AI-powered cybersecurity capabilities for detection engineering, threat hunting, and anomaly detection.--MustEnsure compliance with AI security frameworks and standards such as ISO 42001, ISO 27001, NIST AI RMF, MITRE ATLAS, OWASP, and support AI risk assessments, governance, and regulatory control implementation within banking environments.--MustBanking PreferredJOB PURPOSE:Engineer security by design into the bank's AI systems, ensuring that Agentic AI, Generative AI and traditional machine-learning solutions aredesigned, built, tested, deployed and operated with appropriate security controls throughout their lifecycle. The role builds AI-specific securitycontrols, develops AI-enabled defensive and offensive security capabilities, drives threat modelling and adversarial testing, and contributes to AI risk assessment and governance.AccountabilityAgentic and Generative AI Security Engineering:Design, build and deploy technical controls that protect agentic and Gen AI applications against critical threats,such as prompt injection, insecure output handling, sensitive information disclosure, and unauthorizedinstruction or tool execution. Protect system prompts, and secure RAG architectures, including data ingestion and isolation, retrieval authorization, and source integrity.Agent Identity, Access and Runtime ControlsEngineer controls for agent identity, delegated authority, tool and API permissions, credentials, and inter-agent andtool integrations. Apply least privilege, human approval for high-risk actions, transaction and rate limits, timeouts, circuit breakers and secure failure states so that agents cannot act beyond their authorised scopML and LLM Security Protect ML and LLM models, data, feature stores, model registries and deployment pipelines from critical threats,such as unauthorised access, data poisoning, model theft and extraction, backdoors, and adversarial and evasion attacks. Secure the AI supply chain, including third-party models, datasets, libraries and model artefacts.AI Infrastructure and Cloud SecuritySecure AI workloads across the bank's cloud environments. Configure and operate cloud-native security capabilities for identity and access management,data classification and protection, threat protection, logging and monitoring, and secrets and key management. Harden network, identity and data- protection architecture for AI services.AI-Enabled Cyber CapabilitiesDesign and develop AI solutions that improve the bank's security posture, such as detection engineering, threat hunting, AI red-teaming, anomaly detection, and vulnerability prioritisationAI Threat Modelling and Secure ArchitecturePerform threat modelling and security architecture reviews for AI solutions at the earliest stages of design, and define reusable secure design patterns and reference architectures.Deep Knowledge of AI Security Standards, Regulations, Frameworks and TechnologiesMaintain current knowledge of the standards, regulations, frameworks and technologies relevant to AI security.The examples below are indicative and not exhaustive: Standards, frameworks and regulations: ISO/IEC 27090, 42001, and 27001, NIST AI RMF, MITRE ATLAS and ATT&CK, OWASP Top 10, EU AI Act, UAE data protection law, UAE Information Assurance standards, ... Cloud Security Services: Microsoft Agentic SOC, Defender, Sentinel, Purview, Entra, AWS Bedrock, GardDuty,Experience, Qualifications & CompetenciesMinimum Experience :At least 4 years of experience in cybersecurity, cloud security, application security, security engineering, DevSecOps or security operations, including hands-on experience securing Generative AI, machine-learning or other emerging technology solutions. Practical experience designing and implementing security controls in major cloud environments. Experience with threat modelling,security architecture reviews, vulnerability assessments and adversarial or red-team testing. Experience developing scripts, tools or automation and working with engineering teams in complex enterprise environments, preferably in financial services.Postgraduate qualification inCybersecurity, Artificial Intelligence, Machine Learning, Computer Science, Software Engineering, or a related technical discipline is preferred.Professional Qualifications Relevant professional certifications in cloud security, security operations, AI security or AI governance arepreferred. Examples include Microsoft SC-100, SC-200, SC-300, AI-102 and AZ-500; CISSP, CCSP, CSSLP, OSCP, relevant GIAC certifications (for example GPCS,GCSA, GMLE), ISO/IEC 42001 Lead Implementer or Lead Auditor, AI governance certifications such as AIGP, and CISA or CRISC.Knowledge and SkillsAI, Agentic AI and ML Security Agentic AI architectures, LLM and Generative AI security, ML security, prompt injection and jailbreak defences, guardrails, RAG security, MCP and tool-access security,adversarial machine learning, and AI supply chain security. Familiarity with MITRE ATLAS, the OWASP Top 10s.Microsoft Security and CloudMicrosoft Entra, Defender (XDR, Cloud, Identity), Sentinel (KQL, analytics rules), Purview (DLP, information protection, data governance), Security Copilot and agentic SOC capabilities, Azure AI Foundry and Azure OpenAI security.AI-driven CybersecurityDeveloping AI solutions for defensive and offensive security engineering (e.g., detection engineering, threat intelligence, vulnerability management, red-teaming).Governance and RiskISO/IEC 27090, ISO/IEC 42001, ISO/IEC 27001, NIST AI Risk Management Framework, and the ability to map technical controls to regulatory and policy requirements.Platform and DevSecOpsDocker, Kubernetes / AKS, Helm, Terraform, GitHub Actions, SonarCloud, Trivy.Skills: cloud,ai,security