As an AI DevOps Engineer, you will help build, automate, and maintain infrastructure that powers AI-driven platforms, machine learning workflows, enterprise applications, and cloud-native systems. You will leverage your expertise in DevOps practices, cloud technologies, automation, and deployment pipelines to deliver scalable, secure, and reliable AI solutions.
Your responsibilities may include automating deployments, managing cloud infrastructure, implementing CI/CD pipelines, containerizing applications, monitoring production environments, supporting AI model deployments, and collaborating with software engineering and AI teams.
Responsibilities
- Build and maintain CI/CD pipelines for software and AI applications.
- Deploy, monitor, and manage cloud infrastructure and production environments.
- Automate deployment, testing, and infrastructure provisioning workflows.
- Containerize applications using Docker and orchestration platforms.
- Assist in deploying AI models and machine learning services.
- Monitor application performance, logs, and system health.
- Optimize infrastructure for scalability, availability, and security.
- Troubleshoot production incidents and infrastructure-related issues.
- Collaborate with developers and AI engineers to improve deployment processes.
- Work with Infrastructure as Code (IaC) tools where applicable.
- Follow DevOps best practices while maintaining documentation.
- Continuously learn modern DevOps, cloud, and AI deployment technologies.
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, Software Development, or a related field.
- Strong understanding of Linux systems and networking fundamentals.
- Good analytical and problem-solving skills.
- Basic understanding of DevOps principles and cloud computing.
- Familiarity with containerization and deployment concepts.
- Excellent written and verbal communication skills.
- Ability to learn quickly and work effectively in a collaborative environment.
- Prior internship, academic projects, or DevOps experience is preferred but not mandatory.
Preferred Qualifications
- Experience with Docker and Kubernetes.
- Familiarity with AWS, Microsoft Azure, or Google Cloud Platform.
- Knowledge of CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins.
- Basic understanding of Infrastructure as Code using Terraform or Ansible.
- Experience monitoring systems using Prometheus, Grafana, or similar tools.
- Familiarity with Linux server administration.
- Exposure to AI model deployment frameworks and MLOps concepts.
- Experience with Git and collaborative workflows.
- Understanding of security best practices in cloud environments.
- Strong attention to detail and commitment to reliable infrastructure.
Skills
- DevOps
- Docker
- Kubernetes
- CI/CD
- GitHub Actions
- Jenkins
- Linux
- AWS
- Azure
- Google Cloud
- Terraform
- Infrastructure as Code
- Monitoring
- MLOps
- AI Deployment
- Automation
- Git & Version Control
- Problem Solving
- Team Collaboration
- Communication Skills
Compensation
Projects are compensated based on complexity and duration, with earnings ranging from ₹130/hour to ₹387/hour.
What You'll Gain
- Build scalable infrastructure supporting AI and enterprise applications.
- Gain hands-on experience with cloud platforms, DevOps pipelines, and automation.
- Learn modern AI deployment, MLOps, and production infrastructure management.
- Work alongside experienced DevOps engineers, AI developers, and software teams.
- Develop expertise in cloud engineering, infrastructure automation, and production operations.
- Build a strong foundation for career growth in DevOps Engineering, Cloud Engineering, Platform Engineering, AI Infrastructure, or Site Reliability Engineering.
