Jr DevOps Engineer

About Us: webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. We recognize the evolving demands of a data-driven society for scalability and flexibility, and we firmly believe that the future of AI lies in distributed processing at the edge, bringing computation closer to the source of data generation. Our mission is to build a future where a company's valuable data and intellectual property remain entirely private, enabling the deployment of large-scale AI models directly on standard consumer hardware without compromising the information embedded within those models. We are developing an end-to-end platform that is secure, scalable, and fully under the control of our users, empowering enterprises with AI that understands their unique business. We are a team driven by truth, ownership, tenacity, and humility, and we seek individuals who resonate with these core values and are passionate about shaping the next generation of AI. About the Role: We are seeking a DevOps Engineer to design, build, and scale secure infrastructure supporting AI workloads across cloud and edge environments. This is a high-impact individual contributor role where you will help drive infrastructure architecture, platform reliability, and security best practices across the organization. You will work closely with engineering teams to implement scalable, automated infrastructure solutions that enable our AI platform to operate efficiently across diverse deployment scenarios—from public cloud to hybrid and edge environments. This role requires strong technical depth, production experience, and the ability to translate complex requirements into resilient infrastructure systems. Responsibilities: - Design and implement secure, scalable infrastructure across multi-cloud (AWS, Azure, GCP), hybrid, and edge environments - Build and maintain Infrastructure as Code (Terraform, Pulumi, Ansible) using GitOps workflows and automated validation - Deploy and operate Kubernetes clusters optimized for AI/ML workloads, including GPU scheduling and container security best practices - Develop secure CI/CD pipelines with integrated security controls (SAST, DAST, vulnerability scanning, secrets management) - Support MLOps infrastructure initiatives including model deployment automation, versioning, and lifecycle management - Implement observability and monitoring frameworks using tools such as Prometheus, Grafana, ELK, or Datadog - Enforce security best practices including IAM, encryption, network segmentation, and compliance automation - Participate in incident response, reliability improvements, postmortems, and disaster recovery planning - Develop reusable infrastructure modules and documentation (runbooks, architecture docs, standards) - Mentor junior and mid-level engineers on DevOps best practices and infrastructure design Qualifications: - 1–3 years of experience…