QTLoads is seeking a talented and motivated Software Engineer with expertise in DevOps tools to join our team. As a Software Engineer, you will play a crucial role in automating the IT infrastructure and application deployment processes. Role & responsibilities DevOps and Cloud Automation : Implement and maintain automation pipelines for the deployment of AI/ML-based applications on cloud pltforms (e.g., AWS ) using Docker containers . Application Deployment : Collaborate with the development teams to design and execute efficient and reliable application deployment processes. Infrastructure Automation : Automate infrastructure provisioning and configuration tasks, including VPC setup, server configuration , and network endpoint configuration. Containerization : Assist in containerizing applications using Docker, ensuring scalability, portability, and security. Continuous Integration/Continuous Deployment (CI/CD) : Implement and enhance CI/CD pipelines to streamline the application deployment process. Monitoring and Logging : Set up monitoring and logging solutions to ensure the smooth functioning of deployed applications and identify performance bottlenecks. Security : Work closely with the security team to implement best practices for securing cloud-based applications and infrastructure. Troubleshooting and Support : Collaborate with cross-functional teams to troubleshoot and resolve any infrastructure or deployment-related issues. Documentation : Maintain detailed documentation of infrastructure and deployment processes, ensuring knowledge sharing and seamless handover. Education and Requirements: Educational Background : Bachelor's degree in Computer Science, Engineering, or a related field. Experience: 6-8 years of relevant experience in DevOps, cloud automation, and application deployment. DevOps Tools : Proficiency with popular DevOps tools and technologies such as Git, Jenkins, Ansible, Terraform, Kubernetes , etc. Cloud Platforms : Strong hands-on experience with at least one major cloud platform (e.g., AWS, Azure, GCP) and familiarity with cloud services such as EC2, S3, VPC, etc Docker : Solid understanding and practical experience with Docker containerization for application deployment. AI/ML and Models : Familiarity with AI/ML concepts and experience in deploying applications that use pre-trained or custom models will be advantageous. Scripting and Automation : Proficient in scripting languages such as Python, Bash, or Shell for automating tasks. Networking : Knowledge of networking concepts and experience in configuring network endpoints and VPCs. Problem-Solving : Strong analytical and problem-solving skills to diagnose and resolve technical issues. Communication : Excellent communication and collaboration skills to work effectively with cross-functional teams and clients. Cloud Certifications : Relevant cloud certifications (e.g., AWS Certified DevOps Engineer, Azure DevOps Engineer) will be a plus.