Lead Security Incident Response & Detection Architect

Fluidstack

Austin (TX)

On-site

USD 330,000 - 380,000

Full time

14 days+

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Job summary

Fluidstack is seeking a seasoned Incident Response Lead to secure our frontier compute infrastructure across corporate, cloud, and data center environments. You will own end-to-end IR from detection to eradication, drive cross‑team investigations, and define severity models and on‑call rotations as we scale.

You will build detection logic, response playbooks, and forensic tooling for high‑value targets like AI workloads.

Qualifications

  • Led major security incidents from first alert to resolution under pressure.
  • Built detection logic and response playbooks that caught real intrusions.
  • Conducted digital forensics across cloud, endpoint, and network evidence.
  • Turned incidents into permanent improvements by partnering with security and IT teams.

Responsibilities

  • Lead incident response end-to-end across corporate, cloud, and data center environments from detection to eradication and post-incident review.
  • Build detection logic, response playbooks, and forensic tooling for the frontier compute environment.
  • Run investigations across cloud, endpoint, network and physical systems to form a unified threat view.
  • Stand up the incident response function from ground up with defined severity models and on-call rotations.
  • Turn each incident into a permanent improvement by closing gaps surfaced by investigations.

Skills

Led major incidents
Detection logic
Digital forensics
Incident response program
Threat hunting
Incident reports

Tools

SIEM
SOAR
AWS
GCP

Job description

Fluidstack is seeking a seasoned Incident Response Lead to secure our frontier compute infrastructure across corporate, cloud, and data center environments. You will own end-to-end IR from detection to eradication, drive cross‑team investigations, and define severity models and on‑call rotations as we scale.

You will build detection logic, response playbooks, and forensic tooling for high‑value targets like AI workloads.

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