1 to 25 of 73 vLLM Jobs in the UK

open inference. Knowledge of observability stacks, open Bonus Points Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM). Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and hybrid cloud architectures. Advanced experience with containerization (Docker) and orchestration (Kubernetes), including custom ...

TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex ...

TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex ...

/GenAI platform engineering specifically. You don’t need to tick every box.AI & GenAI Platform Engineering• Model serving and gateway infrastructure (e.g. vLLM, LiteLLM, managed endpoints), with routing, failover, and per-workload cost attribution• Agent orchestration and tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model Context ...

/GenAI platform engineering specifically. You don’t need to tick every box. AI & GenAI Platform Engineering • Model serving and gateway infrastructure (e.g. vLLM, LiteLLM, managed endpoints), with routing, failover, and per-workload cost attribution • Agent orchestration and tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model ...

these. They are a bonus, not a checklist. Experience with Delta Lake, Iceberg, Spark or Databricks, PalantirExperience optimising LLM serving with tools such as vLLM, TGI or TensorRT LLMSearch and ranking experience. For example Elasticsearch or rerankersBackground in time series forecasting, causal inference, recommender systems or optimisationExperience managing cloud costs ...

plus Model fine-tuning practical experience with LoRA/QLoRA, supervised fine-tuning, or RLHF workflows is a plus Inference optimization experience with vLLM, TensorRT-LLM, Triton, or model quantization techniques is desirable Observability tooling practical experience with LLM monitoring, tracing, and evaluation frameworks is a strong plus Familiarity with ...

. LLM observability and evaluation tools such as LangSmith, Langfuse, Arize or Weights & Biases. Experience hosting or serving open‐weight models (for example vLLM, Hugging Face, SageMaker). Awareness of AI governance and regulation, including the EU AI Act, ISO/IEC 42001 and GDPR. ITIL, Kubernetes (CKA/CKAD ...

React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance & observability: Skilled in debugging distributed ...

React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance & observability: Skilled in debugging distributed ...

training framework such as DeepSpeed, Megatron or TorchTitan. LLM/Agent tooling: evaluation harnesses, prompt optimisation, agent frameworks. Inference and edge: TensorRT, ONNX Runtime, vLLM, Nvidia Triton Inference Server, KServe, Nvidia Jetson (e.g. Jetson Thor). Data and platforms: Databricks, Apache Spark, MLflow, SQL, AWS, GCP, Docker. Working at Rowden ...

understanding of how models are trained, how they work internally, and their inherent limitations. LLM Serving stack: Experience with the LLM serving stack (e.g. vLLM) for open weight models for bringing the latest models to internal users. ML Literacy: Experience evaluating probabilistic ML systems and managing model "tool use", contexts ...

reranking Experience evaluating RAG systems properly (e.g. recall@k, faithfulness) Streaming platforms such as Kafka Agentic or multi-hop retrieval, GraphRAG, query rewriting vLLM or similar for on-prem model serving LangChain, LlamaIndex, RAGAS or Langfuse Government, defence or national security programmes Desirable Experience Working Environment Central London, SC-cleared ...

haves Fintech/trading/market-data domain experience or experience as a trading-platform user. Open-source contributions to AI tooling (LangChain, LlamaIndex, vLLM, DSPy, or similar). Production RAG and vector-database experience; experience with code-generation, transpilation, or developer-experience tooling. Logistics Location: Europe (EU/ ...

Proven ability to solve complex problems and work independently in fast-moving environments. Nice to haves: Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference. PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This ...

years of software engineering experience building distributed backend systems or high-throughput microservices. Proven track record in model serving and inference optimization (e.g., TensorRT, vLLM, ONNX Runtime, Triton Inference Server). Strong programming experience in Python, Go, or C++. Hands-on expertise with Kubernetes, container orchestration, GPU scheduling, and infrastructure ...

experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering. Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton ) and an understanding of how to optimize models for GPU deployment. Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized ...

FedRAMP Experience building developer tools, IDEs, code editors (e.g. Monaco, VS Code extensions) or code analysis tooling Experience serving custom or fine-tuned models (vLLM, TensorRT-LLM, Triton or similar) and GPU infrastructure Rust or C/C++ experience Experience selling to and deploying in regulated or security-conscious enterprises ...

install (packaging, versioning, backwards compatibility). Experience with GPU workloads on Kubernetes (device plugins, node scheduling, NVIDIA GPU Operator) and with LLM serving tools (vLLM, Triton, NIM). Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry) and with exposing it for third‐party components. Exposure to ML orchestration tooling (Flyte, Airflow ...

Architectures: Hands-on experience building with OpenAI and Anthropic models/APIs; strong knowledge of LLMs and agent architectures. Tooling: Practical experience with LiteLLM, vLLM, Langfuse (or equivalent LLM observability tooling). Experience with Claude Code/Codex and associated coding-agent workflows is a plus. Evals: Proven ability ...

systems thinking understanding how ML decisions impact infrastructure High ownership and comfort operating in a fast-paced startup environment Nice to have Experience with vLLM or custom inference servers Experience with Kubernetes or containerised ML workloads Experience working in high-throughput distributed systems Background in AI media generation (image, video ...

venues such asNeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, orHPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experienceapplyingpost-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation ...

rail-optimised and fat-tree topologies. Working knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise. Familiarity with datacenter facilities engineering for AI workloads: high-density power ...

deployment pipelines and scheduling across clusters and providers. What You Bring Strong Python and system-architecture skills. Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar. Experience with queues, scheduling, traffic control, and fleet management at scale. Experience with Linux, Docker, and Kubernetes, and with orchestration ...

with ambiguity and wants ownership. Desirable (or willing to learn) Experience with LLMs: fine-tuning (LoRA/PEFT/full), serving and hosting (e.g. vLLM, TGI, Ollama), and distributed or multi-GPU training. Familiarity with GPUs, cloud, and containers (Docker, etc.). Background in optimisation, Bayesian methods, reinforcement learning ...