Software Golang Engineer (Kubernetes) | Gcore | Remote

The world’s digital experiences run on infrastructure and software that keep them fast, reliable, and secure. Gcore is a global provider of infrastructure and software solutions for AI, cloud, network, and security, powering real-time communication, streaming, enterprise AI, and secure web applications. With 210+ edge locations, 50+ cloud regions, and thousands of GPUs, Gcore collaborates with technology partners such as Intel, NVIDIA, Dell, and Equinix. The company has a global team of 550+ professionals building infrastructure and software supporting the entire digital ecosystem.

• Strong proficiency in Go programming with experience in Kubernetes development, including controllers and operators
• Deep understanding of Kubernetes architecture, resource management, and container orchestration
• Experience working with Kubernetes APIs and custom resources (CRDs)
• Solid knowledge of cloud-native technologies and frameworks, including Docker and Helm
• Strong problem-solving skills for complex challenges in distributed systems
• Excellent communication skills and ability to thrive in a collaborative, team-oriented environment

Nice to have:

• Experience with Python programming language
• Experience developing AI/ML pipelines or integrating AI frameworks (e.g., TensorFlow, PyTorch) into Kubernetes
• Understanding of GPU scheduling and optimization in Kubernetes environments
• Knowledge of security best practices in Kubernetes, including role-based access control (RBAC) and container security
• Contributions to open-source Kubernetes projects or cloud-native communities

• Design and implement AI inference and model training cloud products optimized for Kubernetes, including autoscaling inference servers and distributed training jobs across GPU fleets
• Write clean, efficient, and maintainable Go code for Kubernetes controllers, operators, and custom resources supporting AI workloads
• Build APIs, CLIs, and developer tools to simplify deployment, lifecycle management, and monitoring of AI applications
• Develop features optimizing serverless container workflows for AI, ensuring fast cold starts, resource-efficient scaling, and workload isolation
• Contribute to system performance, reliability, and security focusing on AI-specific challenges such as GPU scheduling, job orchestration, and data throughput
• Stay updated on Kubernetes ecosystem advancements and influence product roadmap accordingly

This position is available only under an employment (labor) agreement.

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