Software Engineer (Python, Kubernetes, AI/ML) | Gcore | Remote
Team: Gcore
Location: Remote but only within Poland
Commitment: full time
Workplace Type: remote
What You’ll Do
- Contribute to the development of the Everywhere Inference platform - a Kubernetes-based solution enabling scalable and portable AI inference across a wide range of environments.
- Design and implement APIs and developer tools to simplify deployment, management, and monitoring of AI applications.
- Focus on packaging and integrating new ML models into the platform, using Python and common ML frameworks.
- Optimize serverless container workflows for AI workloads, ensuring performance, scalability, and seamless autoscaling.
- Collaborate with customers to fine-tune ML model performance and support their unique use cases.
- Work with cross-functional teams to improve the AI applications marketplace and ensure smooth model onboarding and lifecycle management.
- Stay current with trends in Kubernetes, machine learning, and MLOps, and help drive innovation within the platform.
What We’re Looking For
- Proficiency with Python, especially in the context of ML tooling or backend development.
- Experience with AI/ML pipelines or integrating machine learning frameworks like TensorFlow or PyTorch into production environments.
- Hands-on experience with vLLM and SGLang
- Familiarity with cloud-native tooling such as Docker, Helm, and related CNCF technologies.
- A problem-solving mindset and genuine interest in working on distributed systems and platform-level challenges.
- Clear communication skills and a collaborative attitude - you enjoy working closely with others to build great solutions.
Nice to Have
- Solid experience with Go programming, particularly in the context of Kubernetes - including building controllers, operators, and working with custom resources (CRDs).
- Strong understanding of Kubernetes architecture, container orchestration, and resource management at scale.
- Understanding of GPU scheduling and performance optimization in Kubernetes.
- Awareness of Kubernetes security practices, including RBAC and container hardening.
- Contributions to open-source projects or involvement in cloud-native or MLOps communities.
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