Software Golang Engineer (Kubernetes)

Gcore

4d•Hybrid

About The Position

At Gcore, we are building the infrastructure and software foundation for an AI-driven world. We are a global provider of infrastructure and software solutions for AI, cloud, network, and security. With a vast network of edge locations, cloud regions, and thousands of GPUs, your work will have a global reach. We are looking for an experienced Golang Engineer to build Kubernetes-native AI infrastructure and tooling for scalable inference and training workloads. You will collaborate with leading technology partners and work on platforms that power digital products used worldwide. Our vision is to connect the world to AI, anywhere, anytime.

Requirements

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, with a passion for tackling complex challenges in distributed systems.
Excellent communication skills and the ability to thrive in a collaborative, team-oriented environment.

Nice To Haves

Experience with Python programming language.
Experience with 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.

Responsibilities

Design and implement AI inference and model training cloud products optimized for Kubernetes, from autoscaling inference servers to distributed training jobs across GPU fleets.
Write clean, efficient, and maintainable Go code to power Kubernetes controllers, operators, and custom resources supporting AI workloads.
Build APIs, CLIs, and developer tools that simplify the deployment, lifecycle management, and monitoring of AI applications.
Develop features that optimize serverless container workflows for AI, ensuring fast cold starts, resource-efficient scaling, and workload isolation.
Contribute to system performance, reliability, and security, with a focus on AI-specific challenges such as GPU scheduling, job orchestration, and data throughput.
Stay on top of Kubernetes ecosystem advancements (e.g., K8s-native ML tooling, scheduling improvements, SIGs) and influence our product roadmap accordingly.