Principal, Grc Automation and Cyber Risk

F5 F5 · Enterprise · Seattle, WA +1

This role focuses on designing, implementing, and scaling automated, data-driven cyber risk and GRC capabilities using AI and Agentic workflows. It involves hands-on software engineering, API development, systems integration, and modernizing risk management processes. The role requires building agentic automation solutions, integrating LLM-based tooling, and engineering automated pipelines for risk quantification and compliance monitoring.

What you'd actually do

  1. Design, build, and evolve end-to-end GRC automation across risk, compliance, policy, and issue management domains — including writing and maintaining Python-based automation scripts, services, and tools.
  2. Integrate GRC workflows with source systems (cloud platforms, vulnerability tools, IAM, SDLC, third-party systems) via RESTful APIs, webhooks, and event-driven integration patterns to reduce manual effort and improve data quality.
  3. Design, build, and deploy Agentic automation solutions — autonomous AI-driven agents capable of reasoning across GRC data, identifying risks, triggering workflows, and recommending actions with minimal human intervention.
  4. Develop and integrate LLM-based or agent-framework tooling (e.g., LangChain, AutoGen, or comparable frameworks) into GRC workflows.
  5. Design, develop, and maintain RESTful and GraphQL APIs that expose GRC data and capabilities to downstream consumers including dashboards, reporting tools, and integrated enterprise systems.

Skills

Required

  • GRC platform architecture
  • workflow automation
  • API development
  • systems integration
  • Python
  • Agentic workflows
  • ServiceNow IRM
  • RESTful APIs
  • GraphQL APIs
  • LLM-based tooling
  • Agent-framework tooling (e.g., LangChain, AutoGen)

Nice to have

  • Cyber risk management expertise
  • Quantitative risk analysis
  • FAIR-aligned methods
  • Python data processing libraries (e.g., pandas, NumPy)
  • Event-driven integration patterns
  • OpenAPI/Swagger documentation

What the JD emphasized

  • hands-on software engineering
  • API development and systems integration
  • Agentic capabilities
  • write, review, and own production-quality code
  • leveraging purpose-built engineering solutions, Python-based tooling, and Agentic workflows
  • supported by automated data pipelines and integrations
  • through scalable API-driven architectures
  • including custom-developed integrations and Agentic automation agents
  • including writing and maintaining Python-based automation scripts, services, and tools
  • via RESTful APIs, webhooks, and event-driven integration patterns
  • Architect and maintain a systems integration layer
  • Engineer automated pipelines
  • leveraging Python data processing libraries (e.g., pandas, NumPy) integration APIs, and Agentic work flows
  • implementing these as code-driven workflows and API-integrated monitoring checks
  • using automated evidence collection scripts and scheduled integrations
  • Design, build, and deploy Agentic automation solutions
  • Agentic issue triage, intelligent remediation recommendations, and autonomous evidence collection
  • Develop and integrate LLM-based or agent-framework tooling
  • Design, develop, and maintain RESTful and GraphQL APIs
  • Own the end-to-end systems integration architecture
  • API governance standards
  • Build and maintain integration middleware, ETL pipelines, and event-driven connectors

Other signals

  • Leveraging AI-enabled and Agentic capabilities
  • Design, build, and deploy Agentic automation solutions
  • Develop and integrate LLM-based or agent-framework tooling