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Platform Engineer

Talent Search Technology — Poland · Posted ~2 hours ago

Skills

Red Hat OpenShift Kubernetes OVN-Kubernetes CNI AI/ML Graph theory Network graph modeling Path analysis TensorFlow Machine learning inference Data science Configuration abstraction frameworks Large-scale network environments

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Summary ✨ AI‑Generated

A platform engineering role focused on designing, deploying, managing, and troubleshooting enterprise container platforms. You will work deeply with Kubernetes and OpenShift, network orchestration technologies, AI/ML inference, graph-based analysis, data science, and configuration abstraction across complex multi-vendor infrastructure.

Highlights

Work on advanced platform engineering across large-scale, heterogeneous infrastructure environments, combining Kubernetes and OpenShift administration with AI/ML, graph analytics, and scalable configuration management.

Description

Proven experience in designing, deploying, managing, and troubleshooting Red Hat OpenShift environments, with strong platform engineering and administration capabilities.Advanced understanding of Kubernetes architecture, including container orchestration, networking, and OVN-Kubernetes/CNI technologies.Strong AI/ML expertise, particularly in graph theory, network graph modeling, and path analysis for complex infrastructure and network environments.Practical experience with TensorFlow, machine learning inference, and deploying AI/ML models for real-world use cases.Solid background in data science, including data analysis, modeling, and deriving actionable insights from complex datasets.Ability to design and work with configuration abstraction frameworks, enabling flexible and scalable management of diverse infrastructure configurations.Experience working with large-scale, heterogeneous network environments consisting of multi-vendor and multi-purpose devices.Hands-on expertise in LLM/SLM technologies, including the development of purpose-built Small Language Models for network and infrastructure domains.Experience with domain-specific model training and fine-tuning, including behavioral modeling, profiling, and adapting AI models to infrastructure and network-specific requirements.