1 to 25 of 62 Ray Jobs in the UK

experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features Contributions to open‐source ML projects or research publications Experience with model observability ...

levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skills Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Experience with container orchestration tools (e.g. Kubernetes, Helm ...

Learn Strong programming knowledge of Python and Spark; proficiency in vector operations using numpy, scipy; experience with distributed computation using multithreading, multi GPUs, Dask, Ray, Polars Familiarity with AWS Cloud services such as EMR, Sagemaker Overall knowledge of the Software Development Life Cycle Solid understanding of agile methodologies including ...

solutions, including RAG, Agentic and Graph RAG techniques Experience building and scaling AI/ML workloads using distributed training/serving frameworks (e.g., Ray) and GPU acceleration (e.g., CUDA) environments. Proficiency with modern AI system architectures and patterns, including RAG, agentic RAG, and multi-agent systems. Familiarity with LLM evaluation ...

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 ...

C++; and Go. Deploy and manage applications using Kubernetes (K8s) and HPC/Grid Computing/Big Data technologies such as Slurm, LSF, Spark, Ray and Symphony in cloud environments. Required Qualifications, Capabilities, And Skills Formal training or certification on Software Engineering concepts and 5+ years applied experience. Expertise ...

experience with cloud‐native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Familiarity with Anaplan or similar enterprise planning platforms Experience with A/B testing and experimentation frameworks for AI features Experience with model observability tools ...

Scikit-LearnStrong programming knowledge of Python and Spark; proficiency in vector operations using numpy, scipy; experience with distributed computation using multithreading, multi GPUs, Dask, Ray, PolarsFamiliarity with AWS Cloud services such as EMR, SagemakerOverall knowledge of the Software Development Life CycleSolid understanding of agile methodologies including CI/CD, application ...

cloud-native ML infrastructure platforms. Knowledge of vector databases (e.g., Pinecone, Weaviate, Qdrant) and embedding models. Experience with model serving frameworks (e.g., vLLM, TensorRT, Ray).Background in forecasting, planning, or analytics applications. Experience with A/B testing and experimentation frameworks for AI features. Experience with model observability tools (e.g. ...

least one systems language (Go, Rust, or C++). Experience working with distributed compute or training systems (e.g., NCCL, PyTorch Distributed, Spark, Ray, Slurm). Familiarity with GPU telemetry systems such as NVML or DCGM and hardware‐level monitoring concepts. Demonstrated experience scaling systems from Proof‐of‐Concept to production ...

Learn Strong programming knowledge of Python and Spark; proficiency in vector operations using numpy, scipy; experience with distributed computation using multithreading, multi GPUs, Dask, Ray, Polars Familiarity with AWS Cloud services such as EMR, Sagemaker Overall knowledge of the Software Development Life Cycle Solid understanding of agile methodologies including ...

building conversational AI solutions, including RAG, Agentic and Graph RAG techniques Experience building and scaling AI/ML workloadsusingdistributed training/serving frameworks (e.g., Ray) and GPU acceleration (e.g., CUDA) environments. Proficiency with modern AI system architectures and patterns, including RAG, agentic RAG, and multi-agent systems. Familiarity with ...

data workloads, along with modern data transformation and orchestration patterns. Ideally, you may also have experience with parallel or distributed training frameworks such as Ray, or with modern data formats such as Parquet and Iceberg. It would also be helpful if you have some experience with Infrastructure as Code (Terraform ...

registries, pipelines, lifecycle management Developer experience: APIs, SDKs, CLIs, notebooks Infrastructure: storage, networking, IAM, observability, infrastructure-as-code Familiarity with tools such as PyTorch, Ray, Slurm, MLflow, Langchain, Hugging Face, vLLM, Triton, or similar. Able to balance performance, cost, usability, flexibility, and reliability. Strong prioritisation, communication, and stakeholder management skills. ...

background in AWS cloud services, high-performance computing, distributed systems, containerised environments, and infrastructure automation. Experience with workload orchestration technologies such as Slurm, Kubernetes, Ray, or equivalent distributed compute frameworks. Demonstrated success bridging research and production environments, enabling rapid experimentation while maintaining operational excellence, governance, security, and reliability. Proven ability ...

SHARP, RDMA/NUMA, or GPU interconnect topologies). Active open-source or production contributions to modern inference frameworks (e.g., vLLM, Triton, TensorRT-LLM, Ray Serve, or TorchServe). Experience leading multi-team technical initiatives or partnering directly with enterprise customers on mission-critical platform launches. Wondering ...

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 ...

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 ...

tuning, lock-free programming, hardware-aware optimization, or FPGA-adjacent systems. Experience with ML/data tooling such as PyTorch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark, or comparable technologies. Understanding of market microstructure, backtesting pitfalls, transaction costs, and the operational needs of live quantitative strategies. Experience in environments ...

FSDP.Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry. Experience with data pipeline orchestration tools such as Airflow, Flyte, Ray, Metaflow, or Argo Workflows. Experience with containerisation and infrastructure tooling such as Docker, Kubernetes, or Terraform. Experience profiling or optimising ML systems, for example using NVIDIA Nsight. Understanding ...

continuous improvement. Familiarity with Model Context Protocol (MCP) for tool interoperability, plus Google ADK, LangGraph/LangChain for agent orchestration and multi‐agent patterns. Ray, Kubeflow, or similar frameworks. Experience embedding controls, audit evidence, and governance in regulated environments. Experience with GPU efficiency, autoscaling strategies, and workload right‐sizing. About ...

multi‐modal representation learning. Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands‐on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP). Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel ...

multi-modal representation learning. Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP). Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel ...

operate a hybrid Python/C++ engineering stack. A large portion of our stack is Python-based (utilising libraries like NumPy, SciPy, PyTorch, Polars, Ray, Plotly, and Dash), but an increasing amount of our most performance-critical systems are written in modern C++ (C++23). We are actively looking ...

software engineering practices to multi-disciplinary teams. Desirable Skills, Qualifications & Experience: Experience leveraging Kubernetes for application deployment, and familiarity with distributed computing frameworks (e.g., Ray, Spark), or specialised batch schedulers/resource managers (e.g., Slurm, Volcano, Kueue). Demonstratedexpertisewith specialised serving engines (e.g.,vLLM,SGLang) or techniques for deploying models ...