Cybersecurity Engineer

Job description

We're hiring a mid senior cybersecurity engineering generalist (individual contributor) to join an established security team that has limited AI experience.

This hire will be the team's practical AI catalyst: applying AI/ML to improve current detection, remediation, DevSecOps, identity, and automation programs; piloting safe AI integrations; and mentoring colleagues to adopt and maintain AI enabled cyber outcomes.

Technologies: Terraform, GitHub Actions, CrowdStrike, Microsoft Sentinel, Zscaler, Azure AD, ServiceNow.

Candidate must be US work authorized.

Why this hire matters (catalyze AI adoption)

• Bring forward thinking, practical AI engineering into existing security programs to reduce risk faster and increase team effectiveness.


• Demonstrate measurable wins (reduced MTTR, fewer false positives, higher coverage) via pilots that the team can operationalize and scale.


• Lower adoption friction by producing reusable artifacts, runbooks, and training so the existing team can sustain AI integrations.


• Ensure responsible AI adoption with model governance, human in the loop controls, and clear rollback/audit procedures.

Core responsibilities (deliver and enable)

• Assess the team's current detection, response, DevSecOps, identity, and automation efforts; identify pragmatic AI opportunities that retrofit into live programs.


• Prioritize and execute AI pilots that show quick, measurable cyber value; document outcomes, safety controls, and operational runbooks for scaling.


• Build AI enabled detection and triage features that integrate CrowdStrike and Microsoft Sentinel telemetry to reduce analyst load and improve prioritization.


• Augment existing SOAR/ServiceNow runbooks with AI assisted enrichment and decisioning while keeping human oversight and audit trails.


• Improve DevSecOps programs by introducing AI assisted IaC checks, secure IaC templates (Terraform), and GitHub Actions automations to prevent misconfigurations.


• Advance Zero Trust and identity engineering (Zscaler, Azure AD) using AI to spotlight risky access patterns and suggest conditional access refinements.


• Produce production ready engineering artifacts Terraform modules, Sentinel analytics, ServiceNow runbooks, GitHub Action snippets, and test harnesses that integrate smoothly into current processes.


• Coach and mentor team members: run brown bags, create playbooks, perform pair programming, and help teammates operate and maintain AI features.


• Maintain rigorous model governance and security controls for AI use: data lineage, access controls, monitoring, explainability, test datasets, and rollback procedures.


• Measure and report security outcomes: MTTR, detection accuracy, analyst time saved, incident volume changes, and coverage improvements.


• Act as an internal advocate for pragmatic AI: balance innovation with safety, compliance, and operational sustainability.

Should have profile (skills + disposition)

• 5 10 years hands on cybersecurity engineering experience delivering production solutions across detection, automation, DevSecOps, identity, or endpoint areas.


• Demonstrated ability to introduce and integrate AI/ML into live security programs with measurable improvements.


• Strong Terraform and GitHub Actions experience for IaC and pipeline security; able to produce reusable modules and CI integrations.


• Operational experience with CrowdStrike telemetry and Microsoft Sentinel analytics/playbooks.


• Experience building ServiceNow/SOAR automations and integrating runbooks with detection tooling.


• Experience with Zero Trust controls (Zscaler, Azure AD conditional access).


• Production scripting/programming skills (Python preferred) and experience deploying automation to live environments with rollback and auditability.


• Strong communicator, educator, and collaborator comfortable mentoring less experienced teammates and producing clear documentation and training artifacts.


• Systems thinker with a pragmatic, risk based approach to prioritization and delivery.

Recruiter screening checklist (team-catalyst focus)

• Request a concrete example where the candidate introduced AI into a team that was new to AI: pilot goals, integration steps, safety controls, team enablement, and measurable outcomes.


• Confirm examples of artifacts the candidate can hand off to an existing team (runbooks, scripts, Sentinel rules, Terraform modules, ServiceNow workflows).


• Probe for teaching/mentoring experience: workshops, onboarding, pair programming, or brown bags delivered to uplift teammates.


• Verify ability to run safe pilots: how they validated models, implemented human in the loop controls, and ensured traceability/auditability.


• Evaluate cultural fit: willingness to work as an IC, build consensus, and drive change incrementally.

Required Skill Profession

Architecture And Engineering