Platform Security Engineer, Aily Labs

• 4+ years of experience in platform security, DevSecOps, or related roles, with expertise in Cloud Security, SDLC Security, or AI Security
• Extensive experience with AWS infrastructure with focus in security services. The same level of exposure within the Azure Cloud is a plus!
• Strong proficiency in Terraform, Kubernetes security, and cloud security configurations, especially within AWS
• Hands-on experience with CI/CD tools like GitHub Actions, integrating security tests and managing secure change management processes
• Experience with Golang and scripting languages for automating security tasks and managing network security (VPNs, DNS, firewalls)
• Proven experience securing Kubernetes clusters, hardening nodes, configuring network policies, and ensuring secure configurations based on industry standards like CIS Benchmarks
• Familiarity with Data Loss Prevention (DLP) tools and cloud security best practices, including IAM and KMS management

Desirable

• Join our Security Engineering Team as a Platform Security Engineer to focus on Data Loss Prevention (DLP), Cloud Infrastructure Security, and SDLC Security
• In this role, you will design, implement, and maintain robust security solutions to safeguard our platform, ensuring best practices are followed across our cloud environments, applications, and AI systems
• Collaborate with cross-functional teams to secure our infrastructure and sensitive data in a fast-paced, cutting-edge startup environment
• We are looking for candidates that are strong in at least 2 of the areas below:
• Cloud Security:
• Design and implement security controls for AWS environments, with a focus on threat detection, mitigation, and compliance
• Conduct cloud security posture assessments and recommend remediations for identified gaps
• Implement DLP strategies and lead incident response activities related to cloud security events
• Work closely with DevOps teams to embed security into cloud infrastructure and deployed applications
• SDLC Security:
• Integrate security into all stages of the Software Development Lifecycle (SDLC), automating security tests within CI/CD pipelines
• Conduct code reviews and implement secure coding practices across applications and infrastructure
• Collaborate with development teams to ensure security is embedded in the design, deployment, and feature development processes
• Enforce change management practices, including secure branching strategies and code review policies in GitHub
• AI Security:
• Implement security measures to protect AI models and datasets from adversarial attacks, data poisoning, and model theft
• Apply privacy-preserving techniques like differential privacy and federated learning to safeguard sensitive data
• Continuously monitor AI models for anomalies and unauthorized changes, ensuring model integrity
• Work with AI and data science teams to secure the AI development, testing, and deployment pipeline