DevOps Engineer AWS, Kubernetes & ML Infrastructure (Part-Time)

DevOps Engineer – ML Infrastructure & Deployment (Part-Time | Remote)

Company: EAX Media

Location: Remote

Experience: 2–4 Years

Job Type: Part-Time (15–20 hrs/week)

Timezone: EST overlap required

Important Eligibility Criteria

• Only candidates serving notice period, currently freelancing, or not working should apply.
• Candidates already working full-time and seeking an additional part-time role will not be considered.

About the Role

We are looking for a DevOps Engineer with experience in AWS cloud infrastructure, CI/CD automation, Kubernetes, and Machine Learning model deployments to support scalable AI-driven applications.

Responsibilities

• Manage AWS cloud infrastructure
• Build and maintain CI/CD pipelines
• Deploy and manage ML models in production
• Work with Docker and Kubernetes/EKS
• Automate infrastructure using Terraform/CloudFormation
• Monitor applications, infrastructure, and ML workloads
• Troubleshoot production issues and deployments
• Collaborate with development and AI/ML teams

Required Skills

• Strong AWS experience
• Docker & Kubernetes/EKS
• Jenkins, GitHub Actions, or GitLab CI/CD
• Terraform or CloudFormation
• Linux administration
• Monitoring tools (Grafana, Prometheus, CloudWatch)
• Experience with ML deployment workflows

Preferred

• Experience with AI/ML or SaaS platforms
• GPU-based infrastructure knowledge
• FastAPI/Flask deployment exposure

Apply

Send your updated resume and relevant DevOps/ML infrastructure experience to tc@eaxmedia.com

Pay: ₹303,890.20 - ₹700,000.00 per year

Benefits

• Flexible schedule
• Work from home

Application Question(s):

• Have you worked on deploying Machine Learning models in production environments?
• Which tools or platforms have you used for ML model deployment?
• Which AWS services have you used for ML or AI-based applications?
• Do you have experience building CI/CD pipelines for ML or AI applications?
• Are you comfortable working with production-grade AI/ML infrastructure and deployment workflows?

Work Location: Remote