GCP | Hybrid Cloud | AI Security)

Lifescale Analytics•Eagan, MN

1d•Hybrid

About The Position

At Lifescale Analytics, we help achieve data-driven decisions by leveraging strategic technologies to create innovative solutions that ultimately help our clients to navigate their ever-changing data landscape. We have an opportunity for a Senior Cloud Security Engineer (Hybrid/Multi-Cloud) in Morrisville, NC; Falls Church, VA; or Eagan, MN areas (Remote support will be considered for highly qualified candidates.) Note: Relocation expenses are not covered by the employer/client. Applicants responding to this position must be a US Citizen and will be subject to a government security investigation and must meet eligibility requirements by currently possessing the ability to view classified government information. The candidate must have lived in the United States for the past 5 years. The Employer will not sponsor applicants for any employment visas, at hiring or in the future, including but not limited to H-1B visas. Corp-to-Corp or subcontract personnel will not be considered for this position.

Requirements

Expert-level knowledge of security architectures in AWS, Azure, and Google Cloud.
Mastery of Terraform, Ansible, or CloudFormation to deploy and manage security configurations at massive scale.
Ability to leverage Databricks to perform deep-dive analysis on billions of logs for threat hunting and efficacy reporting.
Experience securing Kubernetes (EKS/AKS/GKE) and Docker environments, focusing on runtime protection and image integrity.
Proficiency with OAuth 2.0, SAML, and CIAM solutions for large-scale customer and employee authentication.
Proficiency in using Python (PySpark/Pandas) within Databricks to build custom anomaly detection models that go beyond standard SIEM correlation rules.
Knowledge of the OWASP Top 10 for LLMs and experience implementing AI gateways or "firewalls" to monitor and filter AI-generated traffic.
Deep expertise in building "glue code" that connects disparate COTS and custom applications via secure, automated APIs to streamline cross-functional business activities.
The ability to explain to non-technical stakeholders how AI-driven security decisions (like blocking a suspicious $1M commercial transaction) are made and how to handle "false positives" at scale.
A relentless focus on identifying repetitive manual tasks (e.g., firewall rule reviews, access audits) and replacing them with self-healing, automated systems.
8+ years in a technical Cybersecurity Engineering role, with at least 4 years focused on large-scale cloud or hybrid environments, and a portfolio of projects where AI or Machine Learning was directly applied to solve security or operational scaling problems.
Demonstrated experience managing environments with 10,000+ workloads and high-availability requirements for retail/commercial applications.
Experience with CI/CD and GitOps workflows, treating security configurations as code that is automatically tested and deployed.
B.S. or M.S. in Computer Science, Information Security, or a related technical field required.

Nice To Haves

CISSP (Certified Information Systems Security Professional)
CCSP (Certified Cloud Security Professional)
Cloud-specific Professional Security Certifications (e.g., AWS Certified Security – Specialty or Google Professional Cloud Security Engineer).

Responsibilities

Design and implement automated compliance assessments to enforce hardening standards (CIS, NIST) across cloud accounts and on-premises virtualized environments.
Architect and maintain the security of our sprawling asset inventory. Implement data-at-rest and data-in-transit encryption strategies that span from physical data center servers to cloud-native storage.
Develop and secure the "Identity Fabric" linking 600k+ employees and millions of commercial customers. Collaborate with Fraud teams to integrate signals from SIEM and Databricks to detect and block malicious account activity.
Build and manage secure connectivity (Transit Gateways, Service Mesh) between on-premises hypervisors and multi-cloud environments, ensuring consistent policy enforcement.
Partner with the SOC to develop high-fidelity detection logic. Build SOAR playbooks that automate the isolation of compromised cloud workloads or on-premises VMs.
Support ongoing "Purple Team" exercises and control testing to validate that security tools (EDR, WAF, DLP) are performing as intended across all tenants.
Establish security guardrails for the enterprise’s internal and customer-facing AI models. This includes protecting Databricks training pipelines from data poisoning and implementing mitigations for LLM-specific threats like prompt injection and sensitive data leakage.
Drive the transition from manual "click-to-operate" security to Autonomous Security Operations. This involves building advanced SOAR playbooks that use ML-based triggers to perform auto-remediation across hybrid environments without human intervention.
Partner with business units to integrate security "invisibly" into their workflows. Use automation to reduce "security friction" in logistics and retail operations, ensuring that compliance checks (like PCI or SOC2) are performed continuously and programmatically.
Discover and catalog "Shadow AI" usage across the enterprise, ensuring all third-party AI tools meet the enterprise’s privacy and security standards.