Cloud Security Engineer
Hyderabad · Mid Level
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First 72 hours
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Job description
About Quin
Why this role exists
What you'll own
- Identity as the backbone: SSO, MFA, least-privilege access, and clean joiner/mover/leaver flows across all locations.
- SaaS administration and access governance across our tool estate.
- Harden our cloud environment (network, secrets, key management, logging, config baselines) as the portal scales to millions of users.
- Partner with engineering on secure SDLC: dependency and vulnerability management, security review of new services, and remediation tracking.
- MDM across the fleet, disk encryption, patch baselines, and a sane BYOD/company-device policy.
- Centralised logging/alerting, a workable detection baseline, and an incident-response runbook.
- Implement and operate the technical controls behind SOC 2 and ISO 27001, and own continuous evidence collection so audits are boring.
- Feed vendor/third-party security assessments.
- Lightweight, useful security training and phishing resilience for the team.
Must-haves
- 3–6 years in security engineering, cloud security, or IT/infrastructure with a strong security lean.
- Hands-on cloud security on at least one hyperscaler (AWS, GCP or Azure): IAM, networking, secrets/key management, logging, config hardening.
- Solid identity & access fundamentals (SSO/SAML/OIDC, MFA, RBAC) and endpoint management (MDM).
- Working understanding of SOC 2 and/or ISO 27001 controls — you've supplied evidence for an audit, or you can clearly show you know what one demands.
- Comfortable scripting/automating (Python/Bash) and reading infrastructure-as-code.
- Autonomous and pragmatic: you secure things without becoming the department of "no."
Strong pluses
- AWS Security Specialty, AZ-500, CCSP, CISSP, or ISO 27001 Lead Implementer.
- Infrastructure-as-code security (Terraform + Checkov/tfsec) and SIEM experience.
- Prior security work at a product SaaS with an enterprise buyer base.
- AI/LLM security exposure (prompt-injection defence, model/endpoint hardening) — relevant as we run ML on motion data.
- Having stood something up from scratch in a small, scrappy team.
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