about the role
We are looking for a hands-on DevSecOps Engineer to strengthen and scale security initiatives in our fully cloud-native AWS environment. You will help evolve our DevSecOps practices, improve cloud and Kubernetes security, vulnerability management, and perform security reviews across infrastructure and applications. You will work closely with security and engineering teams, making hands-on improvements while contributing to long-term security direction. We value practical, scalable solutions and smart automation, and we expect ownership and a proactive mindset. As we actively adopt AI, use LLMs extensively, and build internal AI agents, security must scale alongside innovation and regulatory requirements – making AI-aware security a key part of this role.
your mission
Cloud & Platform Security
- Continuously improve the security of our AWS and Kubernetes platforms.
- Strengthen IAM, RBAC, encryption, secrets management, and network controls through secure-by-default policy-as-code.
- Manage edge security, including traffic filtering, WAF configuration, and external exposure management.
- Perform security reviews of new services, architectural changes, and platform components.
- Embed automated security controls into SDLC and CI/CD (SAST, dependency and container scanning, policy enforcement).
- Lead vulnerability management processes, including detection, assessment, prioritization, and reporting.
- Integrate automation and AI-assisted tooling to enhance security reviews and reduce manual effort.
- Define and implement security controls for AI infrastructure components, including gateways, MCP servers, and model proxies.
- Identify and mitigate AI-specific risks such as prompt injection, data leakage, and agent privilege escalation.
your profile
- 5+ years of hands-on experience in DevSecOps, Cloud Security, or related fields.
- Strong hands-on experience operating AWS and Kubernetes in production environments.
- Experience implementing security in Infrastructure as Code and CI/CD workflows.
- Solid understanding of cloud security fundamentals such as access control, secrets management, network security, and encryption.
- Familiarity with container security and common application security risks.
- Deep understanding of AI/LLM security risks, including prompt injection, data leakage, model abuse, and agent privilege escalation; hands-on experience securing AI infrastructure components such as LLM gateways, MCP servers, or agent-based workflows.
- Comfortable with scripting and working in Git-based development environments.
- Good communication skills and ability to work effectively with engineering and product teams.
- Comfortable communicating clearly in English, both written and spoken.
- Experience scaling security practices in fa