1 to 25 of 119 Amazon SageMaker Jobs in London

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 ...

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 ...

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 ...

Applications | Prompt Engineering | Responsible AI | AI Evaluation Frameworks LangChain | LangGraph | Semantic Kernel | AutoGen Cloud & AI Platforms Azure AI Foundry | Azure OpenAI | AWS Bedrock | SageMaker | Google Vertex AI | Hybrid/Multi-Cloud Engineering & Platform Python | Java | Scala | TypeScript | SQL | Kubernetes | Docker | Terraform | Bicep | CI/CD | Infrastructure as Code ...

data structures, algorithms, machine learning, data mining, information retrieval, and statistics Knowledge of Kubernetes (AWS EKS) Experience with training models in Databricks and SageMaker Experience working with MLFlow Practical RAG experience - retrieval quality, embeddings, and vector stores; Graph RAG a strong plus Expert knowledge of at least ...

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 ...

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 ...

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 ...

lifecycle. What you’ll be doing Build and maintain ML pipelines for training, evaluation, and deployment using tools like Databricks, MLFlow, Airflow, DBT, Sagemaker, Tecton Support AI scientist creating reproducible, containerized model training environments (on-demand and scheduled), and manage compute at scale (e.g., spot/GPU autoscaling) Define ...

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 ...

. 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 ...

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 ...

architecture strategy. Keep us ahead of AWS roadmaps in priority services, and set the technical bar as the alliance scales. Technical Depth Generative AI: Amazon Bedrock, SageMaker, agentic and generative AI application patterns Data & Analytics: Amazon Marketing Cloud (AMC), AWS Clean Rooms, data lakes, analytics pipelines Customer … Experience: Amazon Personalize, Amazon Connect, serverless architectures Modern Applications: Lambda, Step Functions, API Gateway, container orchestration Integration: Event-driven architectures, microservices, API design How Success Looks Technical win rate on the strategic pursuits you lead Competitive deals turned in our favor by your technical credibility Reference architectures adopted ...

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 ...

experience of building data pipelines using Spark, Python or SQL - Experience working with AWS and its data and AI/ML services such as SageMaker - Experience of big data and lakehouse platforms on AWS (EMR, Databricks or Snowflake) - Experience building data platforms such as Data Lakes, Lakehouses or Data ...

Software Dev Engineer, Amazon Security Automation Job ID: 10498444 | Amazon UK Services Ltd. At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products … NextGen Security Automation team is looking for a skilled and driven Software Development Engineer (SDE II) to build autonomous security solutions that protect Amazon’s customers. As an SDE II, you will leverage AI/ML technologies to automate detection, analysis, and response across proactive, offensive, and defensive security ...

knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics Knowledge of Kubernetes (AWS EKS) Experience training models in Databricks and SageMaker Experience with MLFlow Practical RAG experience with retrieval quality, embeddings, and vector stores Expert knowledge of at least one of AWS, Azure, or Kubernetes ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

Terraform, Jenkins, Cloudformation or any other IAAC. Proven Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer on AWS Sagemaker and similar tools. Knowledge and experience with CI/CD tools. Strong hands-on technical skills in automation (Jenkins, GitLab, Docker, Kubernetes, Shell Scripting ...

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). Software Engineering: Production-grade Python services (FastAPI, AWS Lambda, event-driven patterns) and front-end development with React/Next.js ...

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 ...

Migration tooling: AWS MGN, AWS DMS, AWS DataSync, AWS SCT, and AWS MAP framework Data and AI platform: AWS Glue, Kinesis, Lake Formation, Bedrock, SageMaker — for integrated data platform and agentic AI roadmap phases Apply Azure/GCP knowledge to extend solution coverage on multi-cloud or Azure-primary ...

operationalised ML models in production, owning deployment, monitoring and lifecycle management. Strong programming: Production-quality, testable Python. Cloud expertise: Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus. MLOps tooling: Experiment tracking and registries, workflow orchestration, model serving and feature stores. CI/ ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

confirm they catch degradation before it costs the business. You'll assess CI/CD and deployment controls — Docker, Jenkins, and the AWS SageMaker, S3, Athena, and Lambda environments models run in. You'll evaluate model governance documentation, explainability approaches, and compliance with regulatory expectations on model risk, fairness ...