Jr DevOps Engineer

webAI•Austin, TX

2d•$90,000 - $130,000

About The Position

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.

Requirements

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

Nice To Haves

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

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