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[Neureality] SR. Software Engineer Infrastructure

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SeniorOn-site·KrakówFull-time

#425189·Dodano miesiąc temu·3

Źródło: IT MATCH

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Tech Stack / Keywords

AIGenAI Kubernetes LLMAlgorithmsGo PythonAPI

Podsumowanie oferty

The Senior Software Engineer will build and optimize large-scale, high-performance GenAI inference systems on Kubernetes. The role involves working on distributed inference, performance optimization, reliability, observability, and production-grade deployment at scale. The engineer is expected to make architectural decisions and take ownership of technical outcomes. Collaboration with a small, senior engineering team focused on performance and production quality is also part of the role.

Wymagania

Minimum 5 years of experience as a Software Engineer, with strong software engineering and system design skills
Programming experience in Go and Python
Hands-on experience with the Kubernetes ecosystem, including Operators, service meshes, GitOps, Gateway API, and OpenTelemetry
Experience with cloud platforms
Strong understanding of optimization algorithms and performance engineering
Ability to independently drive technical initiatives from concept to production
Strong systems thinking and debugging skills
Comfort operating in environments with high autonomy and responsibility

Nice to have:

Experience with modern LLM inference frameworks such as vLLM, SGLang, or TensorRT-LLM
Experience with distributed LLM inference frameworks such as llm-d or NVIDIA Dynamo
Contributions to open-source Kubernetes or ML infrastructure projects
GPU performance optimization and profiling experience
Familiarity with CUDA, NCCL, or Triton kernels
Experience running GenAI systems at scale in production

Obowiązki

Build and optimize high-performance Kubernetes-native GenAI inference systems
Work with modern inference stacks such as vLLM, SGLang, TensorRT-LLM, and related tooling
Work with Kubernetes-native distributed LLM inference frameworks such as llm-d and NVIDIA Dynamo
Design and implement optimization algorithms and performance improvements
Improve reliability, observability, deployment, and operational maturity of AI systems
Make architectural decisions and take ownership of technical outcomes
Collaborate with a small, senior engineering team focused on performance and production quality

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