Jobs for Humanity is seeking a remote DevOps Engineer to establish the infrastructure necessary for their AI-driven product, ensuring it remains efficient, secure, and reliable. Daily responsibilities include designing and managing cloud infrastructure using platforms like AWS, GCP, or Azure, as well as building and maintaining CI/CD pipelines for automated deployments. The role also involves implementing infrastructure-as-code with tools such as Terraform or Pulumi, and containerizing services using Docker and Kubernetes as the system scales. Additionally, the engineer will set up observability tools for monitoring and logging, manage costs and performance of AI workloads, and oversee security measures and access controls.
Candidates should have over four years of experience in DevOps, Site Reliability Engineering, or infrastructure engineering, along with strong skills in scripting languages like Bash, Python, or Go. Familiarity with monitoring tools such as Prometheus or Grafana is also required. This position is ideal for individuals with a security-focused mindset and experience in managing infrastructure for AI or machine learning applications. The role is fully remote, making it suitable for those looking for flexible work arrangements.
About the Role
We're looking for a DevOps Engineer to build the infrastructure foundation that keeps our AI-powered product fast, reliable, secure, and cost-efficient. You'll set up the cloud, deployment, and monitoring systems that let a small team ship confidently and scale smoothly through the pilot and beyond.
What You'll Do
Design, provision, and manage cloud infrastructure (AWS, GCP, or Azure)
Build and maintain CI/CD pipelines for fast, safe, automated deployments
Implement infrastructure-as-code (Terraform, Pulumi, or similar) for reproducible environments
Containerize and orchestrate services (Docker, Kubernetes) as the system grows
Set up observability: monitoring, logging, alerting, and tracing across the stack
Manage and optimize the cost, latency, and reliability of LLM and AI workloads
Own security, secrets management, and access controls across environments
Establish backup, disaster-recovery, and incident-response practices for the pilot
4+ years of DevOps, SRE, or infrastructure engineering experience
Strong hands-on experience with at least one major cloud provider (AWS/GCP/Azure)
Proficiency with infrastructure-as-code tools (Terraform, Pulumi, CloudFormation)
Experience with containerization and orchestration (Docker, Kubernetes)
Solid CI/CD experience (GitHub Actions, GitLab CI, CircleCI, or similar)
Strong scripting skills (Bash, Python, or Go)
Experience implementing monitoring and observability tooling (Prometheus, Grafana, Datadog, etc.)
Security-first mindset and experience with secrets and access management
Experience managing infrastructure for AI/ML or LLM-heavy workloads
Familiarity with cost optimization for high-throughput API usage
Experience with GPU infrastructure or inference serving
Early-stage experience standing up infrastructure from scratch
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