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 Senior 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.

This position will be hybrid in Austin Texas or Grand Rapids Michigan.

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

5–8+ years of experience in DevOps, Site Reliability Engineering, or Infrastructure Engineering supporting production systems

Strong expertise with Docker, Kubernetes, and cloud-native architectures

3–5+ years of hands-on experience implementing Infrastructure as Code (Terraform, Pulumi, Ansible)

Experience working with AWS, Azure, or GCP including compute, networking, and managed services

Proven experience building and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, ArgoCD)

Programming experience in Python (preferred), Go, or Bash for automation

Experience implementing monitoring and observability in production environments

Strong understanding of cloud security best practices and access management

Strong communication skills and ability to collaborate cross-functionally

Preferred Skills

Exposure to multi-cloud or hybrid cloud environments

Experience supporting AI/ML or MLOps workflows

Familiarity with service mesh technologies (Istio, Linkerd)

Experience with edge computing or distributed systems

Understanding of cost optimization and cloud efficiency practices