Xenon7

• Predictive AI Threat Modeling: Lead advanced threat-modeling exercises across the bank’s localized AI/ML pipelines, data repositories, and training environments. Anticipate sophisticated attacker behaviors to design preemptive security guardrails before models are moved to production. • AI Red Teaming & Offensive Simulations: Conduct targeted offensive operations and ethical hacking against our on-premises AI infrastructure. Simulate real-world adversarial attacks, including data poisoning, model inversion, membership inference, evasion attacks, and prompt injection. • Framework Mapping with MITRE ATLAS: Implement and operationalize the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework. Map identified vulnerabilities to ATLAS tactics and techniques to build a robust, AI-specific defensive matrix aligned with traditional MITRE ATT&CK. • On-Premises Infrastructure Hardening: Assess and secure the physical and virtual infrastructure hosting our AI workloads, including bare-metal GPU clusters, local containerized environments (Kubernetes/OpenShift), and private data registries, ensuring strict isolation from external threats. • Proactive Risk Management & Early Detection: Collaborate with the SOC and data engineering teams to build predictive detection use-cases and custom alerts. Identify the early warning signs of an AI-targeted attack or data exfiltration attempt before malicious actors can compromise model integrity.

• Establish AI Security Governance Frameworks: Author, implement, and maintain the bank's enterprise-wide AI Security Policy. Align our internal AI governance with international standards (such as NIST AI RMF, ISO/IEC 42001, and OWASP Top 10 for LLMs) while strictly ensuring compliance with evolving Central Bank of Egypt (CBE) regulations and the Egyptian Data Protection Law. • Design & Build Cybersecurity Controls: Architect, enforce, and validate technical and administrative security controls tailored for the entire AI/ML lifecycle (Data Ingestion, Training, Deployment, and Inference). This includes building robust controls against data poisoning, model inversion, prompt injection, and unauthorized data exfiltration. • Secure MLOps Architecture: Define the reference architectures and secure guardrails for our Machine Learning Operations (MLOps) pipelines. Ensure that security checks, vulnerability scanning, and model integrity verifications are seamlessly integrated into our continuous deployment workflows. • Advanced AI Threat Modeling & Risk Assessment: Lead comprehensive risk assessments and threat-modeling exercises on all proprietary and third-party AI implementations. Identify structural risks in algorithmic logic, training data pipelines, and API integrations, translating technical risks into clear business governance metrics. • Third-Party & Vendor AI Governance: Develop and execute rigorous cybersecurity assessment frameworks for evaluating third-party AI tools, cloud-hosted models, and external vendors, ensuring they meet the bank’s strict data privacy and control standards.

• Infrastructure Integration: Design, deploy, and maintain the connective tissue between our on-premises data centers and Azure. • Cloud Infrastructure Management for Data & AI: Provision, configure, and optimize enterprise-grade Azure resources tailored for data workloads, including Microsoft Fabric, Azure Databricks, Azure Data Factory, Synapse Analytics, and Azure Machine Learning environments. • On-Prem & Cloud Security Hardening: Enforce strict identity governance across hybrid environments by aligning on-premises Active Directory (AD) with Microsoft Entra ID. Manage secrets across environments and implement network security boundaries that satisfy Central Bank of Egypt (CBE) regulations. • Automation & Infrastructure as Code (IaC): Standardize deployment processes across both cloud and local infrastructure using Terraform or Azure Bicep, integrating them into CI/CD pipelines via Azure DevOps to support unified DataOps workflows. • Hybrid Disaster Recovery & Monitoring: Architect and test robust Backup and Disaster Recovery (BCDR) strategies that guarantee zero data loss across both local storage arrays and Azure Data Lakes, maintaining continuous visibility through unified monitoring tools.

Role Description As a leading financial institution in Egypt, we are heavily investing in localized AI and Machine Learning capabilities to drive innovation while maintaining total data sovereignty. Our Cybersecurity Department is looking for a highly skilled AI Cybersecurity Engineer with a deeply offensive mindset. Operating entirely within our secure, high-performance on-premises infrastructure, you will act as an internal adversary. Your mission is to proactively threat model, simulate advanced cyberattacks (Red Teaming), and manage algorithmic risks to detect and neutralize vulnerabilities in our AI systems before they can be exploited. Key Responsibilities - Predictive AI Threat Modeling: Lead advanced threat-modeling exercises across the bank’s localized AI/ML pipelines, data repositories, and training environments. Anticipate sophisticated attacker behaviors to design preemptive security guardrails before models are moved to production. - AI Red Teaming & Offensive Simulations: Conduct targeted offensive operations and ethical hacking against our on-premises AI infrastructure. Simulate real-world adversarial attacks, including data poisoning, model inversion, membership inference, evasion attacks, and prompt injection. - Framework Mapping with MITRE ATLAS: Implement and operationalize the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework. Map identified vulnerabilities to ATLAS tactics and techniques to build a robust, AI-specific defensive matrix aligned with traditional MITRE ATT&CK. - On-Premises Infrastructure Hardening: Assess and secure the physical and virtual infrastructure hosting our AI workloads, including bare-metal GPU clusters, local containerized environments (Kubernetes/OpenShift), and private data registries, ensuring strict isolation from external threats. - Proactive Risk Management & Early Detection: Collaborate with the SOC and data engineering teams to build predictive detection use-cases and custom alerts. Identify the early warning signs of an AI-targeted attack or data exfiltration attempt before malicious actors can compromise model integrity. Qualifications - 5+ years of dedicated experience in Offensive Security, Red Teaming, or Advanced Penetration Testing, with a proven pivot and at least 2 years of hands-on experience securing or attacking AI/ML workloads. - Prior experience working within a regulated financial institution in Egypt. Understanding of the security constraints, compliance mandates, and audit rigor required by the Central Bank of Egypt (CBE) for on-premises operations. - Expert-level knowledge of MITRE ATLAS and the OWASP Top 10 for Large Language Models (LLMs). Proven track record of translating these theoretical frameworks into practical, automated offensive playbooks. - Strong proficiency in Python or bash scripting to build custom exploit or testing tools. - Deep understanding of on-premises infrastructure components (Linux administration, private networking, storage area networks). - Direct experience with container security (Docker, Kubernetes) and secure MLOps pipelines. Requirements - Bachelor’s degree in Computer Science, Cybersecurity, Computer Engineering, or a strictly related field. - Highly valued offensive certifications: OSCP, OSCE, CRTP, or GPEN, alongside any specialized AI/ML security credentials. - A relentlessly curious, adversarial mindset balanced with strong risk management disciplines. - The ability to clearly articulate complex, highly technical AI vulnerabilities into actionable remediation plans for data scientists and senior banking executives. - Fluency in English. Benefits - Attractive, market-leading salary package. - Clear career advancement path with professional development opportunities.

Role Description As a premier banking institution in Egypt, we are rapidly scaling our cutting-edge AI and Machine Learning capabilities. To ensure this innovation does not compromise our security posture, our Cybersecurity Department is looking for an expert AI Cybersecurity Architect. In this highly strategic role, you will be the foundational architect behind the bank’s AI security governance. You will not just react to threats; you will build the comprehensive control frameworks, policies, and architectural standards that govern how AI models, Large Language Models (LLMs), and big data pipelines are safely developed, deployed, and managed across the enterprise. Key Responsibilities - Establish AI Security Governance Frameworks: Author, implement, and maintain the bank's enterprise-wide AI Security Policy. Align our internal AI governance with international standards (such as NIST AI RMF, ISO/IEC 42001, and OWASP Top 10 for LLMs) while strictly ensuring compliance with evolving Central Bank of Egypt (CBE) regulations and the Egyptian Data Protection Law. - Design & Build Cybersecurity Controls: Architect, enforce, and validate technical and administrative security controls tailored for the entire AI/ML lifecycle (Data Ingestion, Training, Deployment, and Inference). This includes building robust controls against data poisoning, model inversion, prompt injection, and unauthorized data exfiltration. - Secure MLOps Architecture: Define the reference architectures and secure guardrails for our Machine Learning Operations (MLOps) pipelines. Ensure that security checks, vulnerability scanning, and model integrity verifications are seamlessly integrated into our continuous deployment workflows. - Advanced AI Threat Modeling & Risk Assessment: Lead comprehensive risk assessments and threat-modeling exercises on all proprietary and third-party AI implementations. Identify structural risks in algorithmic logic, training data pipelines, and API integrations, translating technical risks into clear business governance metrics. - Third-Party & Vendor AI Governance: Develop and execute rigorous cybersecurity assessment frameworks for evaluating third-party AI tools, cloud-hosted models, and external vendors, ensuring they meet the bank’s strict data privacy and control standards. Qualifications - 8+ years of progressive experience in Cybersecurity Architecture or IT Risk Governance, with at least 3+ years of dedicated experience explicitly focused on securing AI/ML systems and building enterprise governance frameworks. - Minimum of 3 years of experience working within a regulated financial institution in Egypt. You must possess an intricate understanding of CBE cybersecurity circulars, auditing standards, and banking compliance requirements. - Proven track record of designing, writing, and implementing complex cybersecurity control frameworks from scratch. Deep familiarity with traditional frameworks (NIST SP 800-53, ISO 27001) as well as modern AI-specific guardrails is a must. - Strong conceptual and practical understanding of AI/ML infrastructure, including neural networks, LLM orchestration layers, vector databases, and cloud data platforms (Azure, AWS, or hybrid environments). You must speak the language of both data scientists and enterprise security engineers. - Bachelor’s degree in Cybersecurity, Computer Engineering, Computer Science, or a related technical discipline. - Elite security certifications are highly preferred (e.g., CISSP, CISM, CRISC, or CCSP), alongside any specialized AI security credentials. - Exceptional communication and stakeholder management skills. You must possess the professional maturity to present complex AI risks to C-suite executives and board members, while maintaining the technical credibility to influence data science teams. Fluency in English. Benefits - Attractive, market-leading salary package - Clear career advancement path with professional development opportunities

Role Description We are seeking a seasoned Senior Azure Administrator with a powerful dual background in both Cloud and On-Premises infrastructure to join our elite Data & AI department. In the Egyptian banking sector, compliance and data residency require a sophisticated hybrid approach. In this role, you won't just manage the cloud; you will be the critical bridge connecting our heavy on-premises data legacy systems (like enterprise data warehouses and local servers) with cutting-edge Azure environments. You will ensure seamless data pipelines, high availability, and airtight security across our entire hybrid data estate. Key Responsibilities - Infrastructure Integration: Design, deploy, and maintain the connective tissue between our on-premises data centers and Azure. - Cloud Infrastructure Management for Data & AI: Provision, configure, and optimize enterprise-grade Azure resources tailored for data workloads, including Microsoft Fabric, Azure Databricks, Azure Data Factory, Synapse Analytics, and Azure Machine Learning environments. - On-Prem & Cloud Security Hardening: Enforce strict identity governance across hybrid environments by aligning on-premises Active Directory (AD) with Microsoft Entra ID. Manage secrets across environments and implement network security boundaries that satisfy Central Bank of Egypt (CBE) regulations. - Automation & Infrastructure as Code (IaC): Standardize deployment processes across both cloud and local infrastructure using Terraform or Azure Bicep, integrating them into CI/CD pipelines via Azure DevOps to support unified DataOps workflows. - Hybrid Disaster Recovery & Monitoring: Architect and test robust Backup and Disaster Recovery (BCDR) strategies that guarantee zero data loss across both local storage arrays and Azure Data Lakes, maintaining continuous visibility through unified monitoring tools. - On-Premises Infrastructure (STRICT MUST): Deep, hands-on experience managing traditional on-premises enterprise data centers is non-negotiable. You must have a strong background in virtualization (VMware/Hyper-V), physical networking, storage area networks (SAN), and Windows/Linux server administration before or alongside your cloud career. Qualifications - 5+ years of total experience in IT infrastructure/system administration. - At least 3+ years of dedicated, hands-on experience specializing in Azure Cloud Administration within a complex, hybrid architecture. - Mandatory prior experience working inside a regulated bank or financial institution in Egypt. - Proven familiarity with connecting cloud services to on-premises data repositories (such as SQL Server or Teradata). - Solid knowledge of containerized environments (AKS/Docker) and enterprise data pipelines is highly advantageous. Requirements - Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a relevant technical field. - Microsoft Certified: Azure Administrator Associate (AZ-104) is required. - Preferred: Certifications reflecting hybrid/security expertise, such as Azure Stack Hub/Arc familiarity or Azure Security Engineer (AZ-500). - Exceptional troubleshooting skills for complex hybrid routing or latency issues. - A proactive mindset and fluency in English. Benefits - Attractive, market-leading salary package. - Clear career advancement path with professional development opportunities.

• Build DevOps automations to setup Sagemaker Unified Studio for enterprise • Implement Sagemaker Lifecycle configurations • Create CICD pipelines for end-users to deploy custom Docker images & Kernels in Sagemaker • Build alert & monitoring capabilities for Sagemaker projects to control costs and service quotas • MLOps automations for model and infrastructure deployments to higher environments

Role Description The Sagemaker DevOps Engineer acts as the bridge between standard infrastructure and the specialized needs of Machine Learning (ML). Your primary goal is to take the "manual" out of ML. You will be responsible for architecting the environment (SageMaker), ensuring it scales across an enterprise, and automating the journey a model takes from a developer's laptop to a production environment. Responsibilities - Build DevOps automations to setup Sagemaker Unified Studio for enterprise - Implement Sagemaker Lifecycle configurations - Create CICD pipelines for end-users to deploy custom Docker images & Kernels in Sagemaker - Build alert & monitoring capabilities for Sagemaker projects to control costs and service quotas - MLOps automations for model and infrastructure deployments to higher environments Qualifications - From 6+ years of experience in a relevant role - Expert level in AWS and Python - Working knowledge with Sagemaker - Experience building DevOps automations for enterprise Requirements - Preferred to have experience building Jenkins pipelines - Preferred to have experience with MLOps automations Location Remote (Europe) Employment Type Contractor/Full-time

• Architect Document Intelligence Solutions: Design and implement advanced Machine Learning and Deep Learning models to parse, extract, and interpret text and complex chemical structures from unstructured, scanned PDF documents. • Develop LLM & Retrieval Systems: Build and optimize Large Language Model (LLM) applications, leveraging vector databases to enable semantic search, advanced data interpretation, and retrieval-augmented generation (RAG). • End-to-End ML Pipelines: Own the entire machine learning lifecycle, including data preprocessing (specifically for chemical data and OCR outputs), model training, evaluation, deployment, and post-deployment monitoring. • Bridge Chemistry & AI: Apply your chemistry domain knowledge to translate molecular structures, diagrams, and chemical data into machine-readable formats, embeddings, and actionable insights. • Cloud Architecture & Deployment: Deploy scalable, secure, and production-ready AI/ML pipelines within the AWS ecosystem, ensuring high availability and performance. • Cross-Functional Collaboration: Partner closely with software engineers, data engineers, and domain experts to integrate ML models into the core product architecture and align with business goals.

Role Description We are seeking a visionary and highly skilled Senior Data Scientist to lead the development of a cutting-edge document intelligence and discovery platform. This unique role sits at the intersection of advanced Generative AI, Machine Learning, and Cheminformatics. Your primary mission will be to solve a highly complex unstructured data challenge: transforming scanned PDF documents containing intricate chemical structures into highly searchable, interpretable, and actionable knowledge bases. - Architect Document Intelligence Solutions: Design and implement advanced Machine Learning and Deep Learning models to parse, extract, and interpret text and complex chemical structures from unstructured, scanned PDF documents. - Develop LLM & Retrieval Systems: Build and optimize Large Language Model (LLM) applications, leveraging vector databases to enable semantic search, advanced data interpretation, and retrieval-augmented generation (RAG). - End-to-End ML Pipelines: Own the entire machine learning lifecycle, including data preprocessing (specifically for chemical data and OCR outputs), model training, evaluation, deployment, and post-deployment monitoring. - Bridge Chemistry & AI: Apply your chemistry domain knowledge to translate molecular structures, diagrams, and chemical data into machine-readable formats, embeddings, and actionable insights. - Cloud Architecture & Deployment: Deploy scalable, secure, and production-ready AI/ML pipelines within the AWS ecosystem, ensuring high availability and performance. - Cross-Functional Collaboration: Partner closely with software engineers, data engineers, and domain experts to integrate ML models into the core product architecture and align with business goals. Qualifications - 5+ years of proven experience working as a Data Scientist, with a track record of delivering production-grade machine learning models. - A strong background in Chemistry, Cheminformatics, or a highly related scientific field, with a demonstrated ability to interpret and manipulate complex chemical structures and data types. - Advanced proficiency in Python and deep hands-on experience with the AWS cloud stack (e.g., SageMaker, Lambda, S3, EC2). - Practical, hands-on experience working with LLMs (fine-tuning, prompt engineering, or API integration) and vector databases (e.g., Pinecone, Milvus, Weaviate, or Qdrant). - Robust experience in model development, validation, deployment, and evaluation framework tools (e.g., PyTorch, TensorFlow, Scikit-Learn). - Prior experience with Computer Vision, Optical Character Recognition (OCR), or Document AI systems is highly desirable given the scanned PDF focus. - Strong analytical problem-solving skills, excellent communication, and the ability to thrive in a highly collaborative, cross-disciplinary project environment. Benefits - Ecosystem of Opportunity: You'll be part of a growing network where client engagements, thought leadership, research collaborations, and mentorship paths are interconnected. - Collaborative Environment: Our culture thrives on openness, continuous learning, and engineering excellence. - Flexible & Impact-Driven Work: Whether you're contributing from a client project, innovation sprint, or open-source initiative, we focus on outcomes—not hours. - Talent-Led Innovation: Our Innovation Community isn’t just a knowledge-sharing forum—it’s a launchpad for members to lead new projects, co-develop tools, and shape the direction of AI itself.