Kiber təhlükəsizlik arxitekturası üzrə baş mühəndis

Əsas vəzifələr

• Review existing security technologies, tools, and measures to identify gaps and recommend improvements; develop targeted solutions to protect company systems and data against cyberattacks, unauthorized access, and emerging security threats.
• Lead the end-to-end lifecycle of security systems and tools — from proof-of-concept (PoC) testing and design through implementation and formal handover — ensuring solutions meet both technical and business requirements before go-live.
• Implement and continuously tune advanced protection policies and configurations within WAF and APM solutions, aligning them with the company's security architecture and risk management strategy; proactively take measures to prevent threats, minimize attack surface, and reduce false positive/negative rates in accordance with evolving business requirements.
• Design and advocate for Zero Trust architecture principles across network, identity, and application layers; define segmentation strategies, micro-perimeters, and least-privilege access models.
• Define and govern security architecture for cloud environments (IaaS/PaaS/SaaS), ensuring workload protection, data residency compliance, and alignment with cloud-native security best practices.
• Author and maintain internal security architecture standards, design principles, and baseline configurations; establish a security review gate ensuring all new systems and features meet architectural requirements before go-live.
• Identify and mitigate AI-specific threats including prompt injection (direct/indirect), training data poisoning, model extraction, membership inference, and adversarial inputs; apply OWASP Top 10 for LLM Applications and MITRE ATLAS frameworks to assess and manage AI risk across the organization.
• Ensure integrity of the ML pipeline through artifact signing, model registry governance, dependency and notebook hygiene, and safe handling of model weights; define and enforce a security review gate for AI features prior to launch, along with an acceptable-use standard for AI across the company.
• ML-Based Detection Engineering — Architect detection capabilities using anomaly detection and clustering techniques for alert triage; maintain realistic operational expectations around false-positive rates, data labeling challenges, and model drift in production security environments.
• Hands-on experience with LLM guardrail/firewall products and red teaming frameworks (Garak, PyRIT); deep understanding of the Model Context Protocol (client/server model, stdio vs. HTTP transports, trust boundaries) and OAuth-based authorization design for MCP servers — including token scoping and prevention of confused-deputy attacks.

Tələblər

• Higher education in Information Technology, Information/Cybersecurity, Applied Mathematics, Cybernetics, Computer Science, or a related field; a Master’s degree is preferred.
• Minimum 3 years of experience in cybersecurity or information security, including experience in system administration (Windows/Linux) or network administration.
• Strong written and verbal communication skills in Azerbaijani and English; Russian is an advantage.
• Hands-on experience with WAF and application security.
• Hands-on experience in AI/ML security, including threat modeling for AI systems, securing ML pipelines, and working with LLM security tools.
• Strong knowledge of cybersecurity best practices, security technologies, and compliance frameworks such as NIST, ISO, GDPR, and PCI-DSS.
• Understanding of risk management principles and the ability to analyze complex security challenges and develop effective solutions.
• Experience with cloud and virtualization technologies.
• Strong project management, communication, teamwork, and stakeholder management skills.
• Strong decision-making and analytical skills, with a results-oriented mindset and a proactive approach to innovation and continuous self-development.
• Professional certifications such as CCNA, RHCSA, CySA+, SecurityX, CISSP, CISM, or equivalent are preferred.