1 to 25 of 44 Amazon SageMaker Jobs in the UK

including model hosting, request routing, continuous batching, and KV‐cache optimization Deploy, host, and lifecycle‐manage open‐source and proprietary LLMs on Amazon EKS and Amazon SageMaker, as well as on‐prem and local GPU clusters, using reproducible infrastructure as code and continuous delivery pipelines Implement observability … experience operating LLM inference servers such as vLLM and llm‐d (or directly equivalent serving stacks) Hands‐on experience hosting and serving LLMs on Amazon EKS and/or Amazon SageMaker, and on local GPU infrastructure Knowledge of LLM reliability and risk considerations, including latency/throughput ...

particularly Data Loss Prevention (DLP), Information Protection, and Insider Risk Management, and how these underpin safe enterprise AI adoption Familiarity with Azure AI Foundry, Amazon Bedrock, or Amazon SageMaker; Google Vertex AI experience a bonus Understanding of data readiness principles for AI, including permissions hygiene, oversharing remediation … Software as a Service (SaaS), and serverless architectures Understanding of enterprise AI deployments (e.g., Microsoft 365 Copilot rollouts, Copilot Studio agents, Azure AI Foundry, Amazon Bedrock, Amazon Q Business, Google Vertex AI, or Gemini for Google Workspace) Strong project management and stakeholder communication skills Experience architecting solutions ...

creating pipelines that integrate seamlessly into production environments. Extensive experience in cloud environments (AWS, Azure, or GCP), including leveraging cloud-native AI tools like SageMaker, Vertex AI, or Azure ML Studio. Demonstrable expertise in creating interactive dashboards and visual analytics using tools such as Streamlit, Plotly, Dash, or D3.js. ...

Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred qualifications, capabilities and skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols ...

deploying Machine Learning models using Python Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn or XGBoost Knowledge of AWS cloud services including SageMaker, Lambda and S3 Experience with MLOps tools such as MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with containerisation, IaC, event ...

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 AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

performance and reliability. Build and integrate AI-driven features (LLMs, intelligent APIs, automation workflows). Implement RAG patterns, AI pipelines, and model integrations (Bedrock, SageMaker). Optimize and monitor models for accuracy, drift, bias, and observability. Leverage Claude/equivalent frontier models to engineer solutions Notes on the role ...

/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Kubernetes) to build and manage scalable AI solutions. Strong skills ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

tools, and raising potential risks to senior colleagues. Basic understanding of cloud platforms (AWS, Azure, or GCP); exposure to cloud-native AI tools (e.g., SageMaker, Vertex AI, Azure ML Studio) is a plus. Awareness of containerisation technologies (e.g., Docker); exposure to orchestration tools (e.g., Kubernetes) is beneficial ...

. Hands‐on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry ...

using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data engineering platforms. ...

including CI/CD pipelines, model versioning, and monitoring. Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. Kubernetes) to build and manage scalable AI solutions. Skills in data engineering, including building data pipelines, data wrangling ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery … building RAG systems. Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability. Clear communication with ...

optimization frameworks (AutoPrompt, DSPy) and building evaluation suites. Experience with distributed computing, data sharding, and performance optimization. Hands-on with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. Demonstrated experience in financial services, particularly investment banking operations. ABOUT US J.P. Morgan is a global leader in financial services ...

developing a growing team of Data Scientists Establishing best practice across model development, governance and MLOps Tech Stack Python | SQL | TensorFlow | PyTorch | AWS | Azure | SageMaker | NLP | LLMs | MLOps What we're looking for: Strong commercial Data Science and Machine Learning experience Experience leading teams or acting as a technical ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands‐on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML). Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands-on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML) Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

Familiarity with cloud‐based application development (AWS & Azure). Familiarity with one or more ML frameworks and technologies: scikit‐learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton. Strong communication skills, able to express ideas clearly and collaborate across teams. #J-18808-Ljbffr ...

libraries, frameworks and services : such as ML libraries e.g. TensorFlow or PyTorch model servers such as AWS Bedrock MLOps such as ML Flow or Sagemaker agent/AI orchestration such as LangGraph or MCP libraries Strong programming skills, especially in Python (other languages like C++, Scala, Rust ...