Senior SW Engineer – AI Infrastructure & Optimization

What You’ll Do

• 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 

Requirements

Required Qualifications

• 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 

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