DevSecOps Full Stack Security Engineer

The Full Stack DevSecOps Engineer is responsible for designing, engineering, and delivering security products and platforms from concept through production. The role develops end-to-end solutions spanning front-end applications, back-end services, application programming interfaces (APIs), data pipelines, databases, and cloud infrastructure. The engineer will apply AI and machine learning (AI/ML), where appropriate, to create intelligent automation for security use cases such as vulnerability triage, risk scoring, anomaly detection, and self-service security workflows. The role also embeds secure development practices, Zero Trust principles, and DevSecOps controls throughout the software development lifecycle (SDLC). Working closely with security, product delivery, DevSecOps, and platform engineering teams, this individual will translate security needs and product roadmaps into scalable, reliable, and maintainable engineering solutions. Responsibilities Design, build, test, deploy, and maintain full-stack security products, including user interfaces, APIs, web services, data layers, and cloud-based components. Apply AI/ML and AI-assisted technologies, where appropriate, to automate security processes and improve areas such as vulnerability triage, risk assessment, anomaly detection, and self-service security workflows. Develop clean, secure, well-tested, maintainable code following secure coding principles and software engineering best practices. Participate in solution architecture, design, and code reviews, ensuring Zero Trust principles and DevSecOps controls are incorporated into product design and implementation. Collaborate with security, DevSecOps, product delivery, and platform engineering teams to translate product roadmaps and security requirements into technical deliverables. Design and implement continuous integration and continuous delivery (CI/CD) pipelines using technologies such as Tekton, GitHub Actions, and Google Cloud Build. Integrate security scanning and other security controls into software development and deployment pipelines. Troubleshoot, debug, and optimize applications to improve performance, scalability, security, reliability, and maintainability. Use AI-assisted development tools to improve engineering productivity and code quality while following organizational security and responsible-use standards. Evaluate and prototype relevant emerging technologies across AI, cloud-native engineering, DevSecOps, and cybersecurity. Develop and maintain technical documentation, including solution designs, API documentation, deployment guidance, and operational runbooks. Apply Agile development practices, including sprint planning, daily stand-ups, backlog refinement, and retrospectives. Demonstrate curiosity and continuous learning by evaluating new tools, frameworks, engineering practices, and opportunities to apply AI to security and software engineering challenges. Required Bachelor’s degree in Computer Science, Software Engineering, Cybersecurity, or a related field, or an equivalent combination of education, training, and relevant professional experience. Minimum of 4 years of full-stack software engineering experience, preferably developing cloud-native applications, security products, developer platforms, or DevSecOps solutions. Proficiency in modern front-end technologies such as Angular, React, and TypeScript, with hands-on experience in one or more back-end technologies such as Java/Spring Boot or Python. Experience designing and developing APIs, web services, and data-driven applications. Experience with relational or cloud-based data technologies such as PostgreSQL and Google BigQuery. Hands-on experience with CI/CD and DevOps technologies such as GitHub, GitHub Actions, Tekton, Google Cloud Build, Docker, Artifactory, and Terraform, or comparable technologies. Experience with a major cloud platform. Experience with Google Cloud Platform (GCP) services such as Cloud Run, Google Kubernetes Engine (GKE), and BigQuery is preferred; relevant experience with Microsoft Azure or Amazon Web Services (AWS) is also applicable. Understanding of secure software development practices, including secure coding, vulnerability management, application security, and integrating security scanning into CI/CD workflows. Familiarity with infrastructure as code (IaC), containerization, and cloud-native application delivery. Excellent verbal and written communication skills, with the ability to explain technical concepts to different audiences and collaborate across security, product, and engineering teams. Preferred Experience developing security products, security automation, or self-service security platforms. Experience applying AI/ML, large language models (LLMs), or AI-enabled APIs to software engineering or cybersecurity use cases. Experience with vulnerability management, vulnerability prioritization, risk scoring, or anomaly detection. Working knowledge of Zero Trust architecture and DevSecOps principles. Experience with Kubernetes or managed Kubernetes platforms such as GKE. Familiarity with AI-assisted development tools and modern software engineering productivity tools. #LI-MF2