AI Training Environment Developer - Linux System Administration

Job Description :

This individual contributor role reports to the Manager of the Global Factory IT group. We are looking for a sharp, driven, and autonomy-loving team member to join our IT team to shape and build efficient self-learning applications. The contractor will be involved in developing an AI Development and Model Training environment to support global AI Solutions development needs.

Key Responsibilities :

- Design and develop a global AI Training environment that dynamically allocates compute and GPU resources based on model training requirements.

- Integrate the training environment with the existing Factory MLOps platform for model tracking, cataloging, and deployment using tools such as MLFlow and KServe.

- Develop a common UI portal for Data Scientists and Communities of Practitioners (CoP) to access the environment, and provide APIs for integration with other factory systems.

- Collaborate with teams across different sites and departments to ensure the environment meets stakeholder requirements.

- Implement the environment across Seagates hybrid infrastructure, including AWS cloud and on-premises systems.

- Support departmental usage tracking and billing through a chargeback model.

- Provide technical support to users encountering issues with the environment.

Required Qualifications :

- Bachelors or Masters degree in Computer Science or a related field.

- Outstanding analytical and problem-solving skills.

- Familiarity with containerized environments, Kubernetes/Docker, and Rancher.

- Strong understanding of data structures, microservices application design, network protocols, publish-subscribe models, JSON.

- Proficiency in Python, VueJS, and web services design/development.

- Experience with virtual machines (VMs), containerized systems, and cloud infrastructure basics (AWS).

- Operating system experience in both Linux and Windows.

Preferred Qualifications :

- Proven experience in Linux system administration and containerized system development.

- Hands-on experience with messaging technologies such as RabbitMQ or Kafka.

- Understanding of GenAI, AI/ML solution architecture, and deployment in manufacturing environments.

- Excellent communication skills, stakeholder engagement, and team collaboration.