12 of 12 Permanent vLLM Jobs in Scotland

scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure ...

Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

Strong knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

operating systems, and runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using ...

field. Strong knowledge of distributed systems, operating systems,machine learning systems architecture, Inference serving, and AI Infrastructure. Hands-on experience withLLM serving frameworks(e.g.,vLLM,Ray Serve,TensorRT-LLM,TGI) anddistributed KV cache optimization. Proficiency inC/C++, with additional experience inPythonfor research prototyping. Solid grounding insystems research methodology,distributed ...