Lead Specialist - Agentic AI (Cyber Defence & Resilience)

Position Overview

Role & Responsibilities

AI Implementation with Cyber Context

• Build LLM-based agent systems for specific cybersecurity use cases
• Implement agent workflows using existing frameworks and patterns
• Apply understanding of cyber operations to design appropriate agent behaviors
• Ensure agents align with cybersecurity best practices and operational constraints

Cyber Tool Integration

• Connect AI agents to SIEM, SOAR, EDR, NDR, vulnerability scanners, ticketing systems
• Leverage cybersecurity knowledge to design appropriate integrations
• Understand data flows, alert formats, incident structures, and tool capabilities
• Work with security APIs, webhooks, and data schemas

Cyber-Aware Workflow Design

• Create agent patterns for SOC operations, incident response, vulnerability management
• Apply knowledge of cyber processes to inform agent design
• Understand alert triage, investigation procedures, threat intelligence workflows
• Design agents that fit naturally into existing cyber operational processes

Security Controls & Access

• Define safe boundaries for agent access to cyber systems
• Understand risk levels of different cyber actions and tools
• Implement appropriate controls based on cybersecurity risk assessment
• Apply knowledge of security controls to agent permission models

Knowledge Engineering & Context

• Design RAG and retrieval systems using cyber data sources
• Structure knowledge stores with understanding of cyber assets, vulnerabilities, threats
• Apply cybersecurity knowledge to organize context appropriately
• Work with security logs, alerts, incidents, and threat intelligence feeds

Model Evaluation & Testing

• Test AI models against cybersecurity use cases
• Assess model performance on cyber-relevant tasks (alert analysis, code review, threat intel)
• Understand what good outputs look like from a cybersecurity perspective
• Create test environments using knowledge of cyber scenarios

Requirements

Technical AI/ML Experience

• Hands-on experience building AI/ML systems, LLM applications, or agent-based workflows
• Practical understanding of agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel)
• Experience with RAG, model integration, and prompt engineering
• Proficiency in Python, APIs, and integrating AI with enterprise systems
• Familiarity with frontier models (OpenAI, Anthropic) and/or open-source models
• Understanding of model evaluation, testing, and AI safety considerations
• Knowledge of vector databases, embeddings, and retrieval systems

Combined/Integrated Experience

• Ability to bridge AI/ML capabilities with cybersecurity requirements
• Experience working with both technical and security teams
• Understanding of how AI can enhance cyber operations and its limitations
• Practical experience implementing technology solutions in security contexts
• Ability to translate cyber needs into technical AI implementations
• Experience explaining AI capabilities and limitations to security professionals

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