23 of 23 Remote/Hybrid 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 ...

/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 ...

. 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 ...

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 ...

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/ ...

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 ...

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 ...

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 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 in resource-constrained or high-throughput environments. Familiarity withGitOpsand CI/CD (e.g.,ArgoCD,GitHubActions). Experience managing ...

platform, or AI infrastructure product. Knowledge of GPU inference and model‐serving technologies , including batching, quantization, KV caching, capacity planning, and frameworks such as vLLM, SGLang, or TensorRT‐LLM. Experience with LLM evaluation, benchmarking, and production applications , such as AI agents, coding assistants, and retrieval‐augmented generation systems. Experience with ...

management in development tools and software including CLIs, SDKs and APIs. Knowledge of machine learning frameworks, runtimes, infrastructure such as PyTorch, ONNX, ExecuTorch, Llama.cpp, vLLM and LiteRT. Understanding of model deployment to edge, embedded or heterogeneous computing, with an understanding of trade-offs between accuracy, performance and power consumption. ...

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 ...

Skills and Experience : Experience using software simulators and/or FPGAs Exposure to any of the following: driver development, inference engines like llama.cpp or vLLM, performance analysis of ML and GenAI workloads, knowledge of Neural Network Processing Units (NPU) or Graphics Processing Units (GPU) and how they are used ...

GDPR. Experience with self-hosted LLM/RAG systems, GPU infrastructure, or AI model-serving technologies such as vLLM or TGI. Familiarity with enterprise infrastructure and security technologies such as RHEL/OpenShift, VMware, Windows/AD, PKI, RBAC/ABAC, and SIEM integrations. Experience with threat intelligence sharing standards ...

make these capabilities easy for engineering teams to consume while maintaining appropriate controls around security, performance and availability.* Using technologies such as containers, Kubernetes, vLLM and AI gateway platforms, you will deploy and operate scalable inference services, improve observability and performance, and investigate complex technical issues across the platform. … experience in:* Linux, application and server administration* Containers and container orchestration, such as Docker and Kubernetes* AI inference and model serving platforms, such as vLLM, SGLang or similar* AI gateways and API management, such as LiteLLM, Bifrost or similar* GPU workloads, including performance, utilisation and resource management* Programming and scripting ...

make these capabilities easy for engineering teams to consume while maintaining appropriate controls around security, performance and availability. Using technologies such as containers, Kubernetes, vLLM and AI gateway platforms, you will deploy and operate scalable inference services, improve observability and performance, and investigate complex technical issues across the platform. … experience in: Linux, application and server administration Containers and container orchestration, such as Docker and Kubernetes AI inference and model serving platforms, such as vLLM, SGLang or similar AI gateways and API management, such as LiteLLM, Bifrost or similar GPU workloads, including performance, utilisation and resource management Programming and scripting ...

workloads Demonstrated experience building ML applications and models yourself, not just running or deploying existing ones (e.g. serving a pre-built model via vLLM doesn't qualify on its own) Hands-on experience writing GPU kernels in CUDA/C++, or in Triton Low-level experience with model internals e.g. … wrote High ownership and comfort operating in a fast-paced startup environment Nice to have Experience optimizing inference workloads in GPU environments Experience with vLLM or custom inference servers Experience with diffusion models, LLMs, or multimodal architectures more broadly Experience with Kubernetes, Docker, or containerised ML workloads Experience building internal ...