DevOps Engineer AI Model Training

• Design realistic Dev Ops tasks based on production-style scenarios involving CI/CD, containers, orchestration, infrastructure as code, monitoring, and incident response.
• Write functional reference solutions with clear setup instructions and validation steps.
• Review AI-generated output, identify technical failures, and provide precise corrections with supporting reasoning.
• Maintain a high standard for technical correctness, reproducibility, and solution quality.
• Provide structured feedback that helps improve future task design and AI system performance.

• 3+ years of professional experience in Dev Ops, Site Reliability Engineering, Platform Engineering, or Infrastructure Engineering.
• Strong coding ability in Python, Go, Bash, or Type Script. The work requires production-quality coding ability beyond scripting alone.
• Practical expertise with CI/CD platforms such as Git Hub Actions, Git Lab CI, or Jenkins.
• Hands-on experience with Docker and Kubernetes.
• Experience with at least one major cloud platform: AWS, GCP, or Azure.
• Infrastructure as Code experience using Terraform, Ansible, Pulumi, or Cloud Formation.
• Advanced Linux and Git skills.
• Professional written and spoken English communication skills.
• Ability to commit at least 20 hours per week.

• Experience with observability technologies such as Prometheus, Grafana, Datadog, or Open Telemetry.
• Background in security, secrets management, or platform reliability.
• Open source contributions or technical writing experience.
• Previous experience with AI training data or model evaluation.

Remote contract engagement.

Minimum availability of 20 hours per week.

Work is performed as part of a customer project focused on scalable, modern cloud infrastructure and AI system improvement.

Hourly compensation ranges from $50 to $100.

Candidates should have professional English proficiency and the required Dev Ops, infrastructure, coding, cloud, and Linux experience. Previous AI experience is not required.