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 ...

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 ...

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. ...

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 ...

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 ...

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 ...

capability to guide engineering teams and influence technical strategy. Some other highly valued skills may include: • Experience with MLOps platforms such as Databricks or SageMaker, and familiarity with hybrid cloud strategies (Azure, on-prem Kubernetes). • Strong understanding of AI infrastructure for scalable model serving, distributed training ...

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 ...

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 ...

account. Some skills that will make you stand out: Apache Spark, Iceberg, Kafka, dbt Scikit-Learn, XGBoost, PyTorch, MLFlow,, GraphFrames, Ray AWS (S3, EMR, SageMaker, Lakeformation), Terraform, Docker, GitHub CI/CD Knowledge Graphs ( RAG), graph ML, probabilistic programming, A/B testing Additional Information For everyone, everywhere. ...

training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMaker* Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector ...

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 ...

frameworks (e.g., Python, .NET, APIs, approved SDLC and web frameworks). Design secure and scalable integrations with AWS services such as S3, Redshift, SageMaker, EC2, IAM, and VPC. Cloud, Security & Governance Ensure applications meet security, risk, and data protection requirements, including SSO/MFA, encryption, segregation of duties ...

ability to present complex information through clear and compelling data visualisations, ideally using Tableau Experience of cloud-based data science technologies, ideally including AWS SageMaker, AWS Bedrock and Snowflake , would be highly desirable A passion for continuous learning and staying up to date with emerging data science tools, technologies ...