AI Security Application Engineer

Role Summary

Key Responsibilities

• AI SOC Agent: build triage, enrichment, correlation and investigation workflows integrated with SIEM, EDR, ExtraHop, Imperva, and ticketing. Produce human-readable incident narratives and recommended actions.
• AI Pen testing Agent: build reconnaissance, vulnerability hypothesis, exploitation planning and safe-execution workflows within authorized scopes; integrate with Kali tooling, Burp, Nuclei, and sandboxed execution.
• AI Secure Code Review Agent: build repository ingestion, diff-aware review, SAST-finding triage, and developer-facing explanation workflows integrated with GitLab and Fortify.
• Curate domain knowledge bases (MITRE ATT&CK, D3FEND, OWASP, CWE, CVE, KEV, internal runbooks) for RAG.
• Design and maintain evaluation datasets and golden benchmarks for each agent, track precision, recall, hallucination rate and task success.
• Collaborate with the human SOC, pentest and code-review contractors to capture expert workflows and convert them into agent behaviours; run shadow-mode trials before any autonomous action.
• Implement human-in-the-loop approval gates and blast-radius controls for all state-changing actions.
• Publish per-agent documentation: scope, capabilities, limitations, failure modes, escalation paths.

Goals

• Deliver three production agents (SOC, Pentest, Code Review) that demonstrably reduce analyst/tester/reviewer toil on measured workflows.
• Ensure every agent operates within defined safety boundaries with full auditability and human oversight where required.
• Achieve measurable, monotonic quality improvement on each agent s evaluation benchmark release over release.
• Build trust with the human security teams through transparency, shadow-mode validation and honest limitation reporting.

Specific Objectives (Smart)

• Within 30 days: shadow-mode AI SOC agent triaging a defined alert class with measured quality metrics.
• Within 60 days: AI Secure Code Review agent running on selected repos in advisory mode; baseline evaluation set established.
• Within 90 days: AI Pentest agent running authorized, scoped reconnaissance and reporting tasks in lab environment.
• Within 6 months: all three agents graduated from shadow to advisory/assistive mode with published quality metrics, runbooks and escalation paths.

Timeline & Engagement Model

• 12-month contract.
• Dependent on AI Platform Engineer s MVP (month 2).
• Agent delivery: months 2-9. Hardening and expansion: months 9-12.

Required Skills & Experience

• 5+ years software engineering, with 2+ years building applied LLM/agent systems.
• Deep practical knowledge of prompt engineering, tool-use design, multi-step agent workflows, and evaluation methodology.
• Working knowledge of at least two of: SOC operations, offensive security, secure code review.
• Comfortable reading security tool APIs and SDKs (SIEM, Tenable, GitLab, Burp, Fortify).
• Python proficiency; familiarity with at least one agent framework used by the platform engineer.
• Understanding of responsible-AI practices: evaluation, red-teaming, bias and hallucination mitigation, human oversight design.
• Strong written communication agents must explain their reasoning to human operators.