1 to 25 of 55 Amazon SageMaker Jobs in the UK

including model hosting, request routing, continuous batching, and KV-cache optimization \n 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 … operating LLM inference servers such as vLLM and llm-d (or directly equivalent serving stacks) \n Hands-on experience hosting and serving LLMs on Amazon EKS and/or Amazon SageMaker, and on local GPU infrastructure \n Knowledge of LLM reliability and risk considerations, including latency/ ...

delivery of a production-grade machine learning platform on AWS. What you’ll be doing Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) — including model registry, automated retraining, drift monitoring, and governance gates Lead the migration of a 12-model production suite … e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network ...

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 ...

Rate: Competitive - Outside IR35 Industry: Technology/Data & AI What You’ll Be Doing? Design, build, and maintain a scalable MLOps platform using Amazon SageMaker, covering model training, deployment, pipelines, monitoring, and governance. Lead the migration of a complex suite of production machine learning models from legacy platforms … into SageMaker, ensuring successful delivery and production readiness. Develop and manage CI/CD pipelines that automate model testing, validation, and promotion across multiple environments. Define secure cloud standards, including IAM permissions, encryption, and networking controls for machine learning workloads. Establish reusable MLOps templates, standards, and best practices that ...

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

cost-effective deployment via Docker , Kubernetes (AKS/EKS/GKE) , and Terraform/Bicep . Lead enterprise MLOps initiatives using Azure ML , SageMaker , Vertex AI , MLflow , and Kubeflow , with continuous integration pipelines (GitHub Actions, Azure DevOps, Jenkins, Argo CD). Oversee monitoring and observability using Prometheus , Grafana ...

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 ...

influence architecture, mentor engineers, and guide teams through complex technical decisions. Nice to have Experience with any of the following would be beneficial: AWS SageMaker, Bedrock, Lambda, ECS, EKS, Step Functions, EventBridge, CloudWatch, IAM, S3, ECR, or related services. MLflow, model registries, feature stores, model observability, drift detection ...

scaling AI solutions using at least one major cloud platform: Azure (Foundry, AI Studio, OpenAI, AKS), GCP (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker) Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK Hands-on experience ...

distributed teams Continuous learning mindset with a focus on emerging technologies and innovation Nice-to-Have 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-18808-Ljbffr ...

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.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 ...

including CI/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 ...

technologies like docker, Kubernetes, AWS EKS etc. Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock,AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc. Master's degree or above in Machine learning/data science, computer science, applied mathematics ...

technologies like docker, Kubernetes, AWS EKS etc. Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g. AWS Bedrock,AWS S3, AWS Sagemaker, Azure AI search, Azure OpenAI, Azure blob storage etc. Master's degree or above in Machine learning/data science, computer science, applied mathematics ...

practices 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 AI solutions.Skills ...

practices 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 AI solutions.Skills ...

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 ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

frameworks (LangChain, LlamaIndex, AutoGen). Data Engineering : ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures. Cloud Platforms : AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery). MLOps : Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment ...

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 ...

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 ...

including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud data and AI platforms ...

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. ...

agnostic engineering mindset; able to work across varied client stacks. Experience with cloud AI services and MLOps/LLMOps (e.g., Azure AI Foundry, AWS SageMaker and/or Bedrock). Strong software engineering practices: Python, APIs, containerisation, CI/CD, IaC, testing, observability and production support. Modern ML/ ...