1 to 25 of 44 Amazon SageMaker Jobs in the UK

Description WTW is seeking a Cloud Platform Engineer -AWS to design, build, and operate the enterprise-scale Amazon Web Services (AWS) compute platform that supports our global business services. In this role, you will deliver secure, scalable, and highly automated cloud services through Infrastructure as Code, platform standards, governance … scale, multi-account AWS environment governed through AWS Organizations, organizational units (OUs), and AWS Control Tower. Design, implement, and manage AWS compute infrastructure, including Amazon EC2, Auto Scaling groups, Elastic Load Balancing, Amazon EBS, and standardized machine images (AMIs) built with EC2 Image Builder. Partner with network ...

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 … priority for Amazon. Please consult our Privacy Notice () to know more about how we collect, use and transfer the personal data of our candidates. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive ...

reusable fine-tuning recipes, evaluation frameworks, benchmarking tools, and optimization patterns that accelerate future deployments. Feed field learnings back to AWS service teams, including Amazon Bedrock, Amazon SageMaker AI, and AWS Neuron. Identify opportunities to expand model customization and optimization within your customer engagement. About the teamABOUT … Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

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 ...

frameworks (PyTorch, TensorFlow). Prompt Engineering, Langchain,Langsmith/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex AI) , MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Preferred Delivered AI projects within Agile frameworks Experience on Gen AI Feedback Analysis ...

least one major cloud provider. AWS, GCP or AzureFamiliar with Docker and CI/CD pipelinesExperience with managed ML platforms such as SageMaker, Vertex AI or Azure MLData engineering and MLOpsExperience with data warehouses such as Snowflake, BigQuery or RedshiftWorkflow orchestration using tools like Airflow or DagsterExperience with MLOps ...

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 ...

calibration) • Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust) MLOps & LLMOps • Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector) • Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks • Exposure to fine-tuning ...

strongly preferred) Production ML engineering excellence – Strong understanding of MLOps, containerization (Docker, Kubernetes/AWS EKS), cloud platforms (AWS/Azure including Bedrock, SageMaker, Azure AI Search), workflow orchestration (Airflow), and API development for ML systems Software development fundamentals – Deep understanding of automated testing, source version control, code optimization ...

Collaborate with Data Scientists and Engineers to productionise models. Improve platform scalability, reliability, and automation. Key Skills Strong Python experience. MLOps tooling (MLflow, Kubeflow, SageMaker, or similar). Docker and Kubernetes. CI/CD tooling (GitHub Actions, GitLab CI, Azure DevOps). Cloud platforms (AWS, Azure, or GCP). ...

with serving patterns (e.g., REST APIs, batch inference, event-driven) Experience with orchestration tools (e.g., Airflow, Dagster) and cloud-native ML platforms (e.g., AWS SageMaker, GCP Vertex AI) Ability to influence decision-making regarding infrastructure and tooling, balancing 'build vs. buy' discussions Strong knowledge of Infrastructure as Code (Terraform ...

tidyverse, caret, ggplot, etc.). Proven experience developing, refining, and monitoring NLP models. Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required. Understanding model evaluation methods and metrics. Ability to operationalize non-technical ideas into relevant research designs, features ...

runbooks. Your Story5+ years' experience in ML Engineering, MLOps, or similar roles. Proficiency in Python. Experience with a managed machine learning platform such as Amazon SageMaker or an equivalent service. Practical knowledge of deploying and operating containerized workloads using Docker and Kubernetes. Experience developing or operating model-serving ...

Engineering depth: Strength in backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQLLLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search ...

Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised ...

production Strong Python development skills Hands-on experience with LangGraph, LangChain, LlamaIndex, CrewAI or similar Experience with AWS, particularly Bedrock, Lambda, ECS, EKS or SageMaker Strong understanding of RAG, agentic AI and multi-agent architectures Experience with CI/CD, cloud infrastructure and application deployment Comfortable working directly with ...

offs. Real-time ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent). Cloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes. Engineering mindset: A focus on reliability, maintainability and continuous improvement. ...

Desirable Skills: Application & Integration Technologies C# .NET Entity Framework JavaScript React Vue.js Windows Presentation Foundation (WPF) REST APIs Microservices architecture AI & Data Technologies AWS SageMaker Large Language Model (LLM) integrations AI-assisted analytics solutions Python-based data analysis tooling Architecture & Governance Enterprise cloud architecture Security architecture and secure ...

Desirable Skills: Application & Integration Technologies C# .NET Entity Framework JavaScript React Vue.js Windows Presentation Foundation (WPF) REST APIs Microservices architecture AI & Data Technologies AWS SageMaker Large Language Model (LLM) integrations AI-assisted analytics solutions Python-based data analysis tooling Architecture & Governance Enterprise cloud architecture Security architecture and secure ...

ethics An understanding of data safety in use of Large Language Models Knowledge and experience of either AWS or Azure: AWS (boto3, Bedrock, Sagemaker, Lambda, S3, EC2) Azure (azure Open AI service, Cosmos DB) Python LangGraph Neo4j/cypher Other coding languages/frameworks e.g. Java/.Net ...

Python, Java, etc) in support of Investment Portfolio Management and Construction Utilizing AWS technologies such as Lambda, API Gateway, DynamoDB, S3, Glue, Redshift, EMR, Sagemaker, EKS/ECS. Working in a team with experienced software engineers to identify great projects and features that will help you grow in your ...