1 to 25 of 58 vLLM Jobs in England

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 ...

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 ...

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 ...

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 ...

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 ...

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. Comfortable taking ambiguous problems from discovery ...

also be interested to hear about any experience you have in the following areas: Experience with ML frameworks and LLM inference, such as PyTorch, vLLM, SGLang, llama. cpp or similar Experience analysing and optimizing CPU performance in heterogeneous CPU + accelerator systems Familiarity with agentic AI frameworks and protocols Understanding ...

with the open‐weight model landscape and the trade‐offs between regional and multilingual models Experience with an inference serving engine behind the gateway — vLLM, SGLang, or TGI for production serving; Ollama for lightweight on‐prem — and sizing GPU capacity for self‐hosted models (KV‐cache footprint, concurrent batch size ...

engineering & synthetic data pipelines Agentic frameworks, reasoning, tool use, memory systems Evaluation & benchmarking (LLM-as-judge, safety/alignment metrics) Inference optimisation (quantization, distillation, vLLM/TensorRT-LLM) Requirements: - PhD or Master's in CS/AI/ML/Physics/Maths or equivalent research experience - Strong hands ...