1 to 25 of 171 Amazon SageMaker Jobs in the UK

maintaining cloud-based platforms and infrastructure, with a focus on AWS. Hands on experience with AWS, including practical delivery across core services such as Amazon EC2, Amazon EKS, Amazon RDS, Amazon Redshift, Amazon S3, Amazon VPC, AWS Identity and Access Management (IAM), AWS Lambda … identity management in cloud environments, including AWS-specific best practices. Familiarity with monitoring, logging, and alerting tools (e.g., Prometheus, Grafana, the ELK stack, Amazon CloudWatch, and AWS X-Ray). Experience with version control systems (e.g., Git) and scripting languages (e.g., Python, Bash, Go). Ability to troubleshoot ...

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 ...

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 ...

Service, Azure AI Foundry, Azure AI Search (vector/RAG), Azure Machine Learning, Azure AI Agent Service, Semantic Kernel, Prompt Flow and Content Safety.AWS: Amazon Bedrock (including Knowledge Bases and Agents), Amazon SageMaker, Amazon Q, Amazon OpenSearch (vector), and Guardrails for Amazon Bedrock.Expertise ...

Azure AI Foundry, Azure AI Search (vector/RAG), Azure Machine Learning, Azure AI Agent Service, Semantic Kernel, Prompt Flow and Content Safety. AWS: Amazon Bedrock (including Knowledge Bases and Agents), Amazon SageMaker, Amazon Q, Amazon OpenSearch (vector), and Guardrails for Amazon Bedrock. Expertise ...

models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon Q, or Amazon Rekognition is highly valued. Experience architecting end-to-end ML pipelines including data ...

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 ...

integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hosting Experience with online large language model quality monitoring, including hallucination detection ...

operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacksFamiliarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hostingExperience with online large language model quality monitoring, including hallucination detection ...

integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model hosting Experience with online large language model quality monitoring, including hallucination detection ...

About the role We are looking for an AWS MLOps Engineering Specialist to deploy and operate production-grade machine learning platforms using the AWS SageMaker MLOps framework. This role focuses on enabling the full ML lifecycle- data preparation, model deployment, monitoring, and retraining-through standardised, automated, and governed pipelines. … will work at the intersection of data science, cloud engineering, and DevOps, ensuring models built in SageMaker can be reliably deployed at scale, monitored for drift and performance, and governed in line with enterprise and regulatory expectations. You will play a key role in standardising ML lifecycle practices, automating ...

deliver production‐ready AI solutions in complex professional environments. Strong consulting, stakeholder engagement, problem‐solving, and technical leadership capabilities. Desirable AWS Tools & Technologies experience: Amazon SageMaker - ML development, training, deployment & MLOps Amazon S3 - Scalable data storage & data lakes AWS Glue - Data integration, ETL & preparation Amazon Redshift … Athena - Data warehousing & serverless analytics Amazon EMR - Big data processing & Spark Amazon Bedrock - Generative AI & foundation models AWS Lambda - Serverless data & AI workflows Amazon QuickSight - BI, dashboards & visualisation Python, SQL, R & PySpark - Data science & analytics As an equal opportunities’ employer, we welcome applications from individuals ...

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. ...

/NLU, recommender systems, LLM applications. 4+ experience in deploying LLMs or other Generative AI solutions to production. Expertise in AWS cloud services (e.g., SageMaker, ECS/EKS, Step Functions, Lambda, Glue). Expertise in frameworks like TensorRT-LLM, vLLM, SGLang, HF Optimum and ONNX runtimes. Strong programming skills ...

Docker/Kubernetes). Strong background in data modelling, warehousing, and supporting analytical and ML workloads. Exposure to MLOps tools including MLFlow, kedro and Amazon Sagemaker. Development and maintenance of real time products like APIs using FastAPI/Flask. Operates autonomously with strong ownership and accountability. Strategic thinker ...

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 ...

building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies). Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference. Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge ...

building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies). Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference. Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge ...

frameworks.Inference & 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 frameworks ...

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 frameworks ...

architecture strategy. Keep us ahead of AWS roadmaps in priority services, and set the technical bar as the alliance scales.Technical DepthGenerative AI: Amazon Bedrock, SageMaker, agentic and generative AI application patternsData & Analytics: Amazon Marketing Cloud (AMC), AWS Clean Rooms, data lakes, analytics pipelinesCustomer Experience: Amazon Personalize … Amazon Connect, serverless architecturesModern Applications: Lambda, Step Functions, API Gateway, container orchestrationIntegration: Event-driven architectures, microservices, API designHow Success LooksTechnical win rate on the strategic pursuits you leadCompetitive deals turned in our favor by your technical credibilityReference architectures adopted as reusable standards across the alliancePOC-to-production conversion ...

monitoring.Experience creating automated evaluations for generative AI applications.Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.Experience using services such as AWS SageMaker or AWS Bedrock.Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.Experience managing machine learning workflows using ...

automated evaluations for generative AI applications. Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices. Experience using services such as AWS SageMaker or AWS Bedrock. Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing. Experience managing machine learning ...

automated evaluations for generative AI applications. Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices. Experience using services such as AWS SageMaker or AWS Bedrock. Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing. Experience managing machine learning ...