mokSa.ai - DevOps Engineer - Cloud Infrastructure
mokSa AI
Software Engineering, Other Engineering, Data Science
Hyderabad, Telangana, India
Posted on Aug 22, 2025
Responsibilities
- Design, deploy, and manage cloud-based infrastructure for machine learning workloads, ensuring scalability, reliability, and performance.
- Collaborate with data scientists and machine learning engineers to understand model requirements and operationalize machine learning pipelines.
- Implement CI/CD pipelines for automating the training, testing, and deployment of machine learning models.
- Develop and maintain monitoring and alerting systems to track the performance and health of deployed machine learning models.
- Optimize resource utilization and cost-effectiveness of cloud resources used for machine learning tasks.
- Implement security best practices to protect sensitive data and machine learning models in cloud environments.
- Work closely with software development teams to integrate machine learning models into production applications.
- Troubleshoot and resolve issues related to infrastructure, deployment, and performance of machine learning systems.
- Stay updated with the latest advancements in cloud computing, DevOps tools, and machine learning technologies.
- Document processes, procedures, and configurations related to machine learning infrastructure and deployment pipelines.
- Bachelors degree in Computer Science or related field.
- 2+ Experience with cloud platforms (AWS) and DevOps practices.
- Proficiency in scripting languages (Python, Shell scripting).
- Familiarity with containerization (Docker, Kubernetes) and machine learning frameworks.
- Strong problem-solving skills and ability to work in a fast-paced environment.
- Certification in AWS and Kubernetes are Preferred
- Experience with infrastructure automation tools (Terraform, Ansible).
- Knowledge of machine learning deployment tools (MLflow, Kubeflow).
- Familiarity with cybersecurity principles for cloud-based systems.
- Salary is not a constraint to right candidate - as per market value
