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 webAI
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.
🎯 The Role
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.
✅ Key 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
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
📌 Required Qualifications
1–3 years of experience in DevOps, Infrastructure Engineering, Software Engineering, or a related technical role
Familiarity with Docker and containerized application environments; exposure to Kubernetes is a plus
Hands-on experience or familiarity with Infrastructure as Code tools such as Terraform, Pulumi, or Ansible
Experience working with or learning cloud platforms such as AWS, Azure, or GCP, including basic compute, networking, and managed services
Familiarity with CI/CD concepts and tools such as GitHub Actions, GitLab CI, Jenkins, or ArgoCD
Basic programming or scripting experience with Python, Go, Bash, or similar languages for automation
Understanding of monitoring, logging, and observability concepts
Foundational understanding of cloud security, identity, permissions, and access management
Familiarity with Git and modern software development workflows
Strong troubleshooting and problem-solving skills with an eagerness to learn
Strong communication skills and ability to collaborate with developers and other technical teams
⭐ Desirable Experience
Exposure to Kubernetes or other container orchestration technologies
Exposure to Infrastructure as Code through professional experience, personal projects, internships, or coursework
Familiarity with multi-cloud or hybrid cloud environments
Interest in supporting AI/ML or MLOps infrastructure and workflows
Familiarity with service mesh technologies such as Istio or Linkerd
Exposure to edge computing or distributed systems
Basic understanding of cloud cost optimization and resource efficiency
Relevant entry-level or associate certifications such as AWS Certified Cloud Practitioner, AWS Solutions Architect – Associate, Azure Fundamentals, or Terraform Associate
🎁 Benefits
Competitive salary Comprehensive health, dental, and vision benefits package 401(k) match Equity options $200/month Health & Wellness stipend Continuing Education support $500/year Function Health subscription Free park
