AI Engineer

Responsibilities

• Design and build agentic AI system architecture
• Design and implement LLM-based agent systems using planning, reasoning, tool use, task decomposition, memory, retrieval, model routing, multi-agent coordination and human-in-the-loop workflows.
• The architecture should support single-agent and multi-agent patterns, including supervisor-agent models, specialist agents, shared state, task delegation, context transfer, controlled escalation and reusable workflow patterns.
• Build the agentic AI harness and control plane
• Create the core harness that governs how agents reason, call tools, access data, use memory, hand off tasks, request approvals, log actions and operate within defined safety boundaries.
• The control plane should include autonomy levels, policy enforcement, approval workflows, immutable audit logging, rollback paths, action limits, kill switches and separation between read-only, recommendation-only and action-capable agents.
• Build the AI-to-cyber tool interface layer
• This includes APIs, connectors, webhooks, queues, MCP-style interfaces, service accounts, scoped credentials, session controls, rate limits, error handling and production support patterns.
• Implement secure tool mediation
• Define and build the mechanisms by which agents retrieve information, call tools, trigger workflows and request operational actions.
• Design agent identity and non-human access controls
• Define identity, authentication, authorisation and privilege boundaries for agents, sub-agents, tools, connectors and model workflows.
• Implement least privilege, just-in-time access, scoped credentials, secrets isolation, approval-bound permissions, session boundaries and full auditability for non-human agent identities.
• Secure the agentic AI supply chain
• Define controls for prompts, tools, connectors, MCP servers, plugins, skills, packages, containers, model artefacts, evaluation datasets, and retrieval sources.
• Establish provenance, allowlisting, signing, dependency scanning, sandboxing, version control, change approval and security review for agent components before they are used in production workflows.
• Engineer the cyber data, memory, and knowledge layer
• Design and build RAG, vector search, structured knowledge, knowledge graphs, case memory and context stores for cyber workflows.
• Relevant data may include assets, identities, vulnerabilities, alerts, incidents, detections, controls, playbooks, tickets, service ownership, business criticality, threat intelligence, code, dependencies, prior investigations, and lessons learned.
• Design evidence provenance and source-trust controls
• Ensure agent outputs are grounded in traceable evidence.
• Agent recommendations should reference source systems, alert IDs, log records, code locations, tickets, vuln

Additional Information

The role will define and build the agentic AI harness, control plane, model evaluation framework, AI-to-system interface layer, memory and knowledge architecture, guardrails, observability model and production standards needed to deploy AI agents safely across cyber functions. Cybersecurity knowledge is useful, but not the primary requirement. The core requirement is deep experience building production-grade LLM, agentic AI, ML, automation or platform systems. Cyber domain expertise will be provided by SOC, incident response, vulnerability management, AppSec, cloud security, IAM, GRC, threat intelligence, red-team and security engineering SMEs. The candidate should also have prior experience operating or supporting production systems, so they can design systems that are reliable, observable, auditable, recoverable and supportable. Day-to-day operations may sit with a separate AI platform, engineering or operations team. Scope of Role The role will support agentic AI capabilities across cybersecurity, including security operations, incident response, threat intelligence, detection engineering, vulnerability management, application security, cloud security, identity and access management, GRC, control testing, red teaming, purple teaming, security engineering, email security, data security and executive cyber reporting. The role is expected to turn AI agents and frontier models from isolated experiments into safe, reusable and measurable operational capabilities.

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