Staff DevOps Engineer , webAI
+ Performance-based incentives
Enterprise grade local AI applications
Be an early applicant
Enterprise grade local AI applications
101-200 employees
Be an early applicant
+ Performance-based incentives
101-200 employees
Company mission
To revolutionize the AI industry by laying the foundation for the development of Super Intelligence (SI).
Role
Who you are
- 7+ years of hands-on experience in DevOps, Site Reliability Engineering, or Infrastructure Engineering with proven track record of architecting production systems
- Expert-level proficiency with Docker, Kubernetes (CKA/CKAD preferred), and cloud-native technologies in production environments
- 5+ years implementing Infrastructure as Code with Terraform, Ansible, or Pulumi, managing large-scale (50+) cloud resources
- Deep experience with cloud platforms (AWS, Azure, or GCP) including compute, networking, storage, and managed services
- Proven experience building and scaling CI/CD pipelines with integrated security controls (GitHub Actions, GitLab CI, Jenkins, ArgoCD)
- Strong programming skills in Python (preferred for automation), Bash, or Go for infrastructure tooling and automation
- Production experience with observability and monitoring tools: Prometheus, Grafana, ELK, CloudWatch, Datadog, or similar
- Experience with MLOps workflows: model deployment automation, versioning, and lifecycle management
- Demonstrated experience with GitOps methodologies and declarative infrastructure management
- Strong understanding of security best practices: encryption, secrets management, identity and access management (IAM), network security
- Excellent written and verbal communication skills for technical documentation and cross-functional collaboration
Desirable
- Experience architecting multi-cloud or hybrid cloud environments with portability and interoperability considerations
- Hands-on experience deploying large language models (LLMs) or transformer models at scale with model serving infrastructure
- Expertise in Zero Trust architecture and modern security patterns for cloud-native applications
- Experience with service mesh technologies (Istio, Linkerd) for microservices communication and observability
- Strong understanding of AI/ML infrastructure: feature stores, model registries, A/B testing infrastructure, and model monitoring
- Experience with edge computing deployments and distributed system architectures
- Cost optimization expertise: FinOps practices, resource rightsizing, and cloud cost management
- Experience mentoring or leading technical initiatives across engineering teams
- Certifications: CKA, CKAD, Terraform Associate, AWS Solutions Architect, Azure Administrator, or GCP Professional Cloud Architect
What the job involves
- We are seeking a Staff DevOps Engineer to architect, build, and scale secure infrastructure for deploying AI workloads across cloud and edge environments
- This is a high-impact, staff-level individual contributor role where you will drive infrastructure strategy, lead technical initiatives, and serve as the subject matter expert on cloud architecture, security best practices, and platform reliability
- You will design scalable, automated infrastructure solutions that enable our AI platform to operate efficiently across diverse deployment scenarios—from public cloud to on-premises and edge computing environments
- This role requires deep technical expertise, architectural thinking, and the ability to translate complex requirements into production-ready infrastructure automation
- Design and architect secure, scalable cloud and edge infrastructure for deploying AI workloads across multi-cloud (AWS, Azure, GCP) and hybrid environments
- Build and maintain production-grade Infrastructure as Code (IaC) using Terraform, Ansible, or Pulumi, managing 100+ resources with GitOps workflows and automated validation
- Design and operate production Kubernetes clusters optimized for AI/ML workloads with GPU support, implementing container security, multi-tenancy, and resource optimization
- Implement secure CI/CD pipelines with integrated security controls (SAST, DAST, vulnerability scanning, secrets management) and automated deployment workflows for containerized AI models
- Lead MLOps infrastructure initiatives including model deployment pipelines, versioning, feature stores, experiment tracking, and monitoring for model performance and drift
- Design comprehensive observability and monitoring using Prometheus, Grafana, ELK, or Datadog with distributed tracing, APM, and real-time alerting aligned to SLIs/SLOs
- Implement security best practices including least-privilege access, encryption at rest/in transit, network segmentation, and automated compliance validation
- Lead incident response and reliability initiatives, participate in on-call rotation, conduct post-mortems, and drive continuous improvement for system reliability
- Architect disaster recovery and business continuity strategies with automated backup, failover, and recovery processes
- Develop reusable infrastructure modules and templates to accelerate environment provisioning and standardize deployment patterns across teams
- Mentor mid-level and senior engineers on cloud architecture, DevOps best practices, and platform reliability through design reviews and technical guidance
- Drive technical documentation and knowledge sharing including runbooks, architecture decision records (ADRs), and infrastructure standards
Our take
On a mission to democratize artificial intelligence, WebAI has built a decentralized development platform that minimizes the amount of data required to build AI models. This directly challenges the assumption that big data is intrinsic to AI, and enables for local, ownable and customizable models to be made.
The company's unified execution layer helps to streamline the training and deployment of models in a way which is both scalable and flexible. Enterprises can use their existing infrastructure to run powerful models while ensuring their data never leaves their devices. Plus, they can tailor each model to their needs with no coding needed.
WebAI believes that off-the-shelf AI models lack the flexibility and control required to be used in real-world tasks, which is what its platform helps to combat. This approach has attracted impressive investment that will be used to grow its development efforts and elevate its mission to democratize AI.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (2 rounds)
Sep 2024
$60m
SERIES A
Dec 2022
$17m
SEED
Total funding: $77m
Company benefits
- Grow with us: Equity options so you share in our growth. Build your future as we build ours.
- Family first: Parental leave and fertility support for every stage of life. Your family matters here.
- Plan your future: A 401(k) to help you save and invest. Secure what's ahead with confidence.
- Stay well: Comprehensive health coverage for body and mind. Because balance fuels great work.
- Keep learning: A stipend to explore, study, and sharpen your skills. Growth never stops.
- Work anywhere: Remote-first with flexible time off to recharge. Find balance wherever you are.
Company HQ
Congress Avenue Historic District, Austin, TX
Leadership
David Stout
(Co-Founder & CEO)Alongside WebAI, David is the Founder of Ghost House Technology.
Ethan Baird
(Co-Founder & Chief Research Officer)Previously, Ethan was a Software Programmer at Ghost House Technology and a Web Technician at Revision Marketing and Design.
Tyler Mauer
(Co-Founder & Chief Innovation Officer)Tyler is also a Director of Product & Market at Ghost House Technology, Previously he held Consultant roles at YETi CGI, RANDY'S House, and Shinola.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 20 more jobs at webAI