11 of 11 Ray Jobs in the UK excluding London

generation patternsExperience building AI agents using frameworks such as LangChain, CrewAI, LangGraph, or similar orchestration platformsExperience operating or integrating serving platforms such as KServe, Ray Serve, NVIDIA Triton Inference Server, Text Generation Inference (TGI), alongside vLLM/llm-dFamiliarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed … quality monitoring (e.g., hallucination, toxicity, drift detection) and tracing via OpenTelemetry conventionsContributions to open-source LLM serving or inference projects (e.g., vLLM, llm-d, Ray, KServe, Triton)ABOUT USJ.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments ...

Experience building AI agents using frameworks such as LangChain, CrewAI, LangGraph, or similar orchestration platforms Experience operating or integrating serving platforms such as KServe, Ray Serve, NVIDIA Triton Inference Server, Text Generation Inference (TGI), alongside vLLM/llm-d Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock … monitoring (e.g., hallucination, toxicity, drift detection) and tracing via OpenTelemetry conventions Contributions to open-source LLM serving or inference projects (e.g., vLLM, llm-d, Ray, KServe, Triton) ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations ...

history of direct contributions to vLLM or similar high-performance open-source ML/AI projects (e.g. PyTorch, Hugging Face TGI, TensorRT-LLM, Ray). Strong understanding of LLM inference mechanics (e.g. KV caching, continuous batching, memory management, model quantisation). Experience interacting with, and upstreaming code to, active open ...

building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms Experience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed … detection, toxicity filtering, and drift detection using open telemetry conventions Contributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton) ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations ...

patternsExperience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platformsExperience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacksFamiliarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model … hallucination detection, toxicity filtering, and drift detection using open telemetry conventionsContributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton)J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy ...

building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms Experience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed … detection, toxicity filtering, and drift detection using open telemetry conventions Contributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton) #J-18808-Ljbffr ...

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 ...

Experience handling time-series challenges such as timestamp precision, sequence gaps, duplicate and out-of-order events. Experience with distributed processing (Spark, Polars, Dask, Ray or similar). Experience with Kafka or similar streaming technologies. Familiarity with PostgreSQL, ClickHouse, Snowflake, Databricks, kdb+ or similar. Docker, Linux, Git and CI/ ...

cases and/or to improve product/system performance, quality and accuracy. Near Real-Time and Batch Inferencing: Use infrastructure like Spark and Ray to stand up inferencing services that integrate with operational/analytics workloads. ML Infrastructure: Help build a first-class machine learning platform from the ground … machine learning on real recommendations use cases (brownie points for productionised sequential learning use cases!). Experience with ML/distributed ML frameworks like Ray, Spark-MLlib, TensorFlow etc. Experience with real-time scoring/evaluation of models with low latency constraints. Great coding skills and strong software development experience ...

cases and/or to improve product/system performance, quality and accuracy.Near Real-Time and Batch Inferencing: Use infrastructure like Spark and Ray to stand up inferencing services that integrate with operational/analytics workloads.ML Infrastructure: Help build a first-class machine learning platform from the ground up which … applied machine learning on real recommendations use cases (brownie points for productionised sequential learning use cases!).Experience with ML/distributed ML frameworks like Ray, Spark-MLlib, TensorFlow etc.Experience with real-time scoring/evaluation of models with low latency constraints.Great coding skills and strong software development experience ...

depth profiling of large-scale inference pipelines, specifically focusing on KV cache management and heterogeneous memory scheduling. AI Serving: Optimising high-throughput frameworks (vLLM, Ray Serve, PyTorch Distributed) to ensure low-latency, multi-tenant performance. Research Leadership: Contributing to top-tier venues (OSDI, NSDI, EuroSys, MLSys) and driving those innovations … Stack: Strong proficiency in C/C++ for systems work, with Python for rapid prototyping. Expertise: Hands-on experience with LLM serving frameworks ( vLLM, Ray Serve, TensorRT-LLM ) and distributed algorithms. Mindset: A solid grounding in systems research methodology and performance profiling tools. The "Value Add" (Desired): A PhD focused ...