1 to 25 of 55 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 ...

that fuel analytics, AI/ML, and data-driven operations.* Design and build high-performance data integration solutions: Use AWS Glue, AWS Step Functions, Amazon EventBridge, and AWS Database Migration Service (DMS) to orchestrate ingestion, transformation, and movement of data across on-premises and cloud sources into Amazon … Design and oversee the delivery of secure lakehouse and warehouse architectures: Implement logical and physical data models, data quality controls, and governance practices using Amazon S3 (data lake), AWS Lake Formation, Amazon Redshift, and Amazon Athena to ensure trusted data.* Ability to design AI/ML-enabled ...

Technical Stack & Requirements: AI/ML Stack: Amazon SageMaker, AWS Bedrock. Design Focus: Vector databases, knowledge graphs, and semantic layers. Databricks: Experience with Databricks and Databricks Genie is highly desirable. Core Mandate: Define the standards for "AI-ready" data curation and direct the integration of AI methodologies into … architectures. Excellent communication and stakeholder management skills. Preferred Qualifications Experience with Agentic AI frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel). Experience with AWS Bedrock, Amazon SageMaker, Azure OpenAI, or Vertex AI. Experience implementing Responsible AI frameworks. Financial services, fintech, or investment management domain experience. Experience building recommendation engines ...

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 ...

based programming languages & good working knowledge of python. Experience building and operating data pipelines and analytical systems using technologies such as Google BigQuery, Amazon Athena, or ClickHouse. Experience with distributed data processing frameworks such as Apache Spark and/or Apache Flink. Experience with messaging and streaming systems such … Exposure to agent-based architectures and emerging protocols such as the Model Context Protocol (MCP). Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) #ICBCareer #ICBEngineering ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice ...

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. ...

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 ...

RFPs/tenders; experience running hands‐on product demos independently. Strong knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML services (e.g., SageMaker, Vertex AI, AzureML), including sovereign, on‐premise, and hybrid deployment models. Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks ...

/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. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools ...

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 ...

functional, and globally distributed teams. Continuous learning mindset with a focus on emerging technologies and innovation. Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI). Front-end development (React) or backend development (.NET/C#). Commercial awareness and understanding of business value delivery #J ...

training,evaluationand production deployment, includingMLOps, 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,Azureor GCP; experienced with ...

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. ...

skills in Python, with experience building scalable services, APIs, and production-quality code Experience with modern ML tooling and platforms (e.g.Databricks,MLflow, Airflow, Kubeflow, SageMaker, Vertex AI) Familiarity with CI/CD pipelines and infrastructure-as-code (e.g.Terraform, CloudFormation) Experience building robust, maintainable, and testable ML pipelines and APIs ...

Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines). Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI) Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

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 ...