SC Cleared Lead AI Solutions Architect SME

Lead AI Solutions Architect SME

Clearance required: SC Clearance.

Nationality requirement: UK/Dual Nationality

Location: Remote work with occasional visits to client site (London), currently every other Tuesday

Job Description:

Description and Location

AI solution Architect:

Lead the technical discovery and solution options analysis for applying AI to augment Cyber Security Operations Centre (SOC) activities. The role will assess the current SOC technology and data landscape, identify viable AI use cases, and define a secure target architecture and delivery roadmap.

Key Skills

AI solution architecture and technical design

Generative AI, LLMs, RAG and agentic AI patterns

Cyber security and SOC technology understanding

SIEM, SOAR, EDR/XDR and security tooling integration

Data architecture, APIs and integration patterns

Cloud and AI platform architecture

AI security, assurance and responsible AI

Technical options analysis and feasibility assessment

Human-in-the-loop and AI control design

Stakeholder engagement and technical communication

Key responsibilities

• Assess the existing SOC architecture, including SIEM, SOAR, EDR/XDR, threat intelligence, case management and security data platforms.
• Assess SOC data availability, quality and suitability for AI-enabled use cases.
• Identify opportunities for AI augmentation across areas such as alert triage, enrichment, investigation, threat intelligence, incident summarisation, detection engineering and reporting.
• Assess appropriate AI patterns, including machine learning, RAG, generative AI, assistants and agentic approaches.
• Determine where conventional automation or process improvement is more appropriate than AI.
• Define integration patterns between AI services and existing cyber platforms.
• Identify security, privacy, assurance and human-in-the-loop requirements.
• Assess risks including hallucination, prompt injection, sensitive-data exposure and inappropriate autonomous actions.
• Prioritise use cases based on value, feasibility, data availability, complexity and risk.
• Identify technical dependencies, constraints and implementation considerations.

Key outputs

• AI use-case and feasibility assessment
• Data and integration requirements
• Build vs Buy options assessment
• Security and assurance considerations
• Architecture and implementation roadmap
• Technical risks, dependencies and assumptions