Cloud Infrastructure & AI Operations Engineer (On-Site Only)

Pioneer Circuits Inc.•Santa Ana, CA

7d•$95,000 - $115,000•Onsite

About The Position

We are building the cloud and data backbone for a manufacturing floor that is becoming genuinely intelligent — AI-driven scheduling, computer-vision inspection, digital work orders, and a unified data layer running in AWS GovCloud. This role builds and runs that infrastructure. This is a hands-on engineering role, not a systems administration role. You will make architecture and / or tooling decisions, write infrastructure as code, build pipelines, instrument systems so they explain themselves, and work directly alongside our AI and Automation engineers to get models and services into production inside a regulated environment. We are deliberately open on years of experience, with one exception: you need to have worked in AWS. Everything we are building runs there, and we are not in a position to teach the platform from scratch. Beyond that, we would rather hire a sharp engineer with strong fundamentals, real automation instincts, and obvious momentum than someone with a longer resume and less curiosity. If you have provisioned AWS with Terraform, shipped through CI/CD, containerized an application, and wired up an AI-powered tool because you wanted to see if it would work — you are the profile we are looking for. Defense and compliance experience is something we will teach you.

Requirements

AWS experience. Demonstrable, hands-on experience building and operating on AWS — not classroom or certification-only exposure. You should be able to walk us through services you have personally deployed and what went wrong the first time. We expect working familiarity with the core set: IAM, S3, EC2, VPC and networking, CloudWatch, and Lambda, plus at least one managed service you have used in anger
AWS through infrastructure as code. You have provisioned AWS resources through code rather than the console — Terraform preferred, CloudFormation or CDK accepted
Certification. AWS certification at any level — Cloud Practitioner, any Associate, or higher. If your hands-on depth outruns your credentials, say so in your application; we will consider it, and you would be expected to certify within your first 90 days
Hands-on experience writing infrastructure as code, ideally Terraform
You have built or meaningfully contributed to a deployment pipeline in Azure DevOps, GitLab, Jenkins, GitHub Actions, or similar
Practical proficiency in Python and Bash.
You have built and run containerized applications with Docker or Podman
Experience with a monitoring or observability platform such as Datadog, CloudWatch, Grafana, or Splunk
You have built something real with modern AI tooling — an LLM-backed application, a RAG pipeline, a chatbot, or an ML workflow. Personal and academic projects count
You write documentation people can actually follow, and you can explain a technical decision to someone in operations or quality
U.S. citizens or lawful permanent residents

Nice To Haves

Advanced AWS certification — Solutions Architect Professional, DevOps Engineer Professional, or Security Specialty
Any exposure to AWS GovCloud, or to AWS under a compliance regime or exposure to a regulated or compliance-driven environment — CMMC, FedRAMP, ITAR, SOC 2, HIPAA, or similar
Container orchestration on ECS, EKS, or Kubernetes
Experience with cloud cost management or FinOps practices
Familiarity with AI/ML platforms such as SageMaker, MLflow, Bedrock, or Kubeflow
Front-end or full-stack ability (React, Next.js, TypeScript) for building internal tools and dashboards
Exposure to manufacturing systems, SCADA, MES, industrial IoT, or edge computing
Certifications such as Terraform Associate, CKA, Security+, or CISSP

Responsibilities

Build and maintain infrastructure as code using Terraform (or CloudFormation/CDK) so environments are repeatable, reviewable, and version-controlled
Write automation in Python, Bash, or PowerShell to remove manual steps and speed up recurring work
Build and maintain CI/CD pipelines for infrastructure and application deployment using GitLab CI.
Apply configuration management at scale using Ansible, AWS Systems Manager, or similar tooling
Design, deploy, and operate secure cloud environments across AWS GovCloud and Microsoft 365 GCC High, supporting containerized applications and serverless architectures
Package and run containerized services using Docker, growing into orchestration on ECS or EKS
Build with managed and serverless services — Lambda, S3, API Gateway, Step Functions — rather than standing up servers by default
Partner with our AI engineers to deploy and operate AI/ML and LLM-backed workloads, including inference endpoints, data pipelines, and retrieval systems
Deploy centralized logging, distributed tracing, and real-time monitoring across hybrid cloud and on-premise environments using CloudWatch, or similar platforms
Automate monitoring configuration and alerting as code so observability ships with the infrastructure rather than after it
Track and alert on cloud cost and usage changes, and surface optimization opportunities to engineering leadership
Use AI-assisted anomaly detection and log analysis to find problems before they reach the floor, and grow into defining SLIs, SLOs, and error budgets for critical systems
Support continuous compliance with CMMC, NIST SP 800-171, ITAR, and DFARS requirements as we prepare for third-party assessment
Implement identity and access management, single sign-on, least-privilege policy, and encryption in transit and at rest
Document systems, configurations, and control evidence to a standard that holds up under audit
Contribute to endpoint management and EDR/XDR operations across Windows, Linux, and macOS