What is the salary for this DevOps Engineer role?

Salary information is not publicly listed for this position. Apply directly to discuss compensation with Model N.

Where is this DevOps Engineer position located?

This is an on-site position at Model N located in Hyderabad India, Asia.

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Model N is actively hiring for Engineering roles. Visit the company page to see all open positions and learn more about working at Model N.

DevOps Engineer

Model N Hyderabad India 1 day ago

engineering

We are looking for an enthusiastic and detail-oriented DevOps Engineer to join our team. The ideal candidate should have a solid foundation in DevOps practices, cloud infrastructure, and deployment pipelines, along with exposure to artificial intelligence (AI) technologies. This role will support the automation and optimization of AI model deployment and cloud-based infrastructure, ensuring seamless integration between development, operations, and AI initiatives.

Job Responsibilities

Develop, deploy, monitor, optimize and maintain build and release pipelines.

Support developments teams on branching and versioning approaches

Creating packages, builds, releases and patches as well as the software deliverables for the customers.

Assist in the design, implementation, and management of DevOps pipelines for deploying and maintaining AI models and services.

Automate the deployment of AI applications and models into cloud environments (AWS, Azure, GCP, etc.).

Work with AI and Data Science teams to ensure seamless integration of machine learning workflows into production environments.

Implement CI/CD pipelines for continuous testing and deployment of AI models and applications.

Collaborate with cross-functional teams to troubleshoot and resolve issues related to the integration of AI models, data pipelines, and infrastructure.

Support infrastructure as code (IaC) practices using tools like Terraform, Ansible, or CloudFormation.

Maintain and optimize cloud infrastructure for cost-efficiency and scalability, particularly for AI workloads.

Participate in the implementation of containerization and orchestration technologies (Docker, Kubernetes) for All applications.

Stay updated with the latest trends in DevOps and AI, recommending best practices for improvement.

Work with QA teams to improve efficiency of automation runs by exploring innovative approaches like parallelization/auto scaling etc.