Principal DevOps Engineer

About the Role

Reva.AI is a unified access control and AI security platform designed for modern agentic and cloud-native environments. As organizations adopt AI agents, LLM-powered applications, and distributed cloud services, Reva.AI enables security and product teams to implement dynamic, intent-aware access controls and enforce least privilege across human, machine, and AI identities. The platform combines AI workload discovery, behavioral analytics, policy governance, and runtime enforcement to provide a robust trust layer for AI-driven systems at scale. Guided by a mission to secure the AI era with context-aware, continuously enforced policies, Reva.AI aims to redefine enterprise security with a unified authorization control plane for applications, infrastructure, data, and AI systems.

Responsibilities

• Lead the design, implementation, and maintenance of scalable, secure, and highly available infrastructure for Reva.AI's platform.
• Architect CI/CD pipelines, optimizing build and deployment processes.
• Automate infrastructure provisioning and configuration using modern DevOps and cloud-native practices.
• Collaborate closely with engineering, security, and product teams to ensure reliable releases.
• Implement observability and monitoring, upholding best practices for performance, reliability, and compliance.
• Troubleshoot complex production issues.
• Mentor other engineers and improve operational efficiency.
• Drive the adoption of new tools and processes that support secure AI and access control workloads.

Qualifications

• Strong experience with DevOps practices, including CI/CD pipeline design, build automation, and release management.
• Deep expertise in cloud platforms (such as AWS, GCP, or Azure) and cloud-native technologies (containers, orchestration, infrastructure as code).
• Proficiency in scripting and automation languages (such as Python, Bash, or similar) to streamline operations and deployments.
• Solid understanding of security, identity, and access control principles, especially in AI-driven or distributed systems environments.
• Hands-on experience with monitoring, logging, and observability tools to maintain system health and performance.
• Proven ability to design and operate high-availability, scalable, and resilient systems in production.
• Experience mentoring and guiding engineering teams, with strong collaboration and communication skills.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Background in enterprise security, AI platforms, or authorization systems is highly beneficial.