76 to 87 of 87 Auto-Scaling Jobs in the UK

under load. Ensure implementations support horizontal and vertical scaling, efficient resource utilisation, and predictable behaviour as demand grows. Guide and review approaches to autoscaling, and workload management, aligned with agreed architectures. Review cloud implementations to confirm they meet expectations for availability, scalability, cost-efficiency, and compliance. Operational Excellence & Risk ...

maintain high-performance, high-IOPS cloud storage solutions, ensuring seamless data synchronisation, backup strategies, and optimal throughput. Automate the deployment, configuration, and auto-scaling of workload orchestrators, container platforms, and core service components to ensure high resource utilisation and cost efficiency. Enforce strict security protocols across all environments … workloads on AWS using AWS ParallelCluster, SOCA or custom Slurm fleets. Ability to architect for maximum cost efficiency, implementing automated spot-instance utilization, auto-scaling strategies, and Savings Plans optimization. Proficiency in monitoring infrastructure health and application performance using tools like Prometheus, Grafana, ELK stack, or CloudWatch, with ...

optimization, capacity management, scalability, reliability and cloud cost efficiency. You will likely bring experience in many of the following areas: Kubernetes platform engineering Cluster autoscaling and workload optimization Public cloud services (AWS, Azure or GCP) DevOps and CI/CD practices Site Reliability Engineering (SRE) FinOps and cloud cost management … sees the bigger picture and understands how your team contributes to it. Job Requirements Deep practical knowledge of Kubernetes workload optimization, including rightsizing, autoscaling, scheduling, capacity management and cost‐performance trade-offs. Exceptional communication skills. You have a genuine talent for bringing clarity to complex technical concepts for a wide ...

thrive. What you’ll do... Compose, build, and operate production‐grade Kubernetes clusters for high‐volume model inference and scheduled training jobs. Configure autoscaling, resource quotas, GPU/CPU node pools, service mesh, Helm charts, and custom operators to meet reliability and efficiency targets. Implement GitOps workflows for environment configuration … 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 working for us... Our focus is to ensure we're inclusive every day, building an organisation that reflects modern ...

real-world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators Maximize compute efficiency across the fleet by autoscaling and orchestrating production, research, and experimental workloads Build and operate production-grade deployment pipelines for releasing new models to users Provide high-performance inference infrastructure … accelerator platforms (GPUs, TPUs, or emerging hardware) Representative projects Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads Building production-grade deployment pipelines for releasing new models to millions ...

weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). … throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ...

harden RAG pipelines (indexing, retrieval policies, grounding, guardrails) and agent frameworks. Take basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost tuning. Participate in on‐call for your area and drive root‐cause analysis with crisp follow‐ups. 15% Collaborate Pair … evals; hands‐on with time‐series analysis (forecasting, change‐point, drift). Cloud & ops: Basic infra ownership on GCP (or AWS/Azure): networking, autoscaling, CI/CD, IaC, observability, and cost control. Communication: You explain results clearly, align stakeholders, and write crisp docs. Bonus points DevOps wizardry; GPU/ ...

resource isolation. Additionally, you will implement advanced optimizations—including micro-batch schedulers, speculative decoding, and KV-cache reuse—while refining capacity planning, autoscaling policies, and mentoring engineers across the team. Who You Are Bachelor’s or Master’s degree in Computer Science or a related technical field (or equivalent practical … improving tail latency (P95/P99) and service reliability through metrics-driven engineering work. Experience defining and owning SLIs/SLOs, managing capacity planning, autoscaling policies, and driving post-incident remediation. Preferred Experience with low-level systems and high-performance computing components (e.g., C++ development, CUDA kernels, NCCL/SHARP ...

# DevOps EngineerMid LevelLondonPosted 7 days ago### Role OverviewWe're actively seeking a highly skilled DevOps Engineer to revolutionize our operational capabilities. Your core responsibilities will center on designing unshakeable automation processes, managing massive cloud ...

offering. ResponsibilitiesDefine Fuse's inference serving strategy and architecture from first principlesDesign and build the serving stack: request routing, batching, scheduling and autoscaling for high-throughput, latency-sensitive inference workloadsOwn model-level optimisation strategy for serving, deciding where and how to apply quantisation, distillation, speculative decoding and similar techniques … playbook: this is a founding role shaping a new function, not joining an established oneBonus: Triton or custom ML inference/training frameworks; autoscaling or capacity planning for large-scale inference; multi-tenant serving or SLA-driven infrastructure; background at a hyperscaler, frontier AI lab or large-scale distributed inference ...

offering. Responsibilities Define Fuse's inference serving strategy and architecture from first principles Design and build the serving stack: request routing, batching, scheduling and autoscaling for high-throughput, latency-sensitive inference workloads Own model-level optimisation strategy for serving, deciding where and how to apply quantisation, distillation, speculative decoding … playbook: this is a founding role shaping a new function, not joining an established one Bonus: Triton or custom ML inference/training frameworks; autoscaling or capacity planning for large-scale inference; multi-tenant serving or SLA-driven infrastructure; background at a hyperscaler, frontier AI lab or large-scale distributed ...

role. Responsibilities Define Fuse's inference serving strategy and architecture from first principles. Design and build the serving stack: request routing, batching, scheduling, and autoscaling for high-throughput, latency-sensitive inference workloads. Own model-level optimisation strategy for serving - deciding where and how to apply quantisation, distillation, speculative decoding … still early-stage, not joining an established one. Nice to Have Experience with Triton or custom ML inference/training frameworks. Experience with autoscaling or capacity planning for large-scale inference workloads. Exposure to multi-tenant serving or SLA-driven infrastructure. Background at a hyperscaler, frontier AI lab, or large ...