Join Anicalls as a Kubernetes AI Workload Engineer and play a key role in building and managing AI infrastructure. This position is perfect for someone who enjoys working with cutting-edge technology and collaborating with various teams to deliver impactful solutions.
As a Kubernetes AI Workload Engineer at Anicalls, you will be responsible for designing, building, and supporting containerized AI workloads. Your work will involve creating a robust infrastructure that can efficiently run machine learning and Generative AI applications in a production environment. You will collaborate closely with business, data, and engineering teams to ensure that the AI platforms are secure, scalable, and measurable.
Your day-to-day tasks will include deploying and managing AI workloads on Kubernetes, optimizing GPU utilization for various AI tasks, and using tools like Helm and Docker for packaging and deployment. You will also be involved in troubleshooting any issues that arise with Kubernetes clusters, networking, and deployment processes.
This role is ideal for someone who has a strong background in Kubernetes and AI workloads, and who enjoys working in a collaborative environment. You should be comfortable with troubleshooting and optimizing complex systems, and have a passion for leveraging technology to solve real-world problems.
Key requirements for this position include expertise in Kubernetes, experience with AI workloads, and proficiency in Docker and Helm. Familiarity with machine learning concepts and networking will be beneficial, but the primary focus will be on building and managing scalable AI infrastructure.
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