1 to 25 of 58 Permanent Amazon SageMaker Jobs in London

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 ...

model choices to technical and non-technical stakeholders Develop clean, well-tested, production-ready Python code Build, train and deploy models on AWS using Amazon SageMaker and related services Tune model performance through hyperparameter optimisation, feature engineering and error analysis Instrument models in production, defining and tracking MLOps … learn), writing maintainable, production-quality code. AWS: Hands-on experience building, training and deploying ML models on the AWS stack, ideally with experience in Amazon SageMaker Model tuning & MLOps: Practical experience in hyperparameter tuning and performance optimisation. You can articulate the metrics you'd track for MLOps (e.g. ...

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 TensorFlowStrong experience with AWS ML services (SageMaker, Lambda, S3) in production environmentsStrong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluationProven track record transitioning models from experimentation ...

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 ...

learning or AI workloads through production-grade data engineering solutions Highly Desirable Strong AWS data platform experience (S3, Glue, EMR, Redshift, Athena, Lambda, Bedrock, SageMaker or equivalent) Experience operationalising machine learning models into production environments Experience with MLOps, model deployment, model monitoring, model lifecycle management, and automated validation processes ...

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 ...

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 ...

Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure ...

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 ...

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). ...

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 ...

scikit, pytorch, 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, and model ...

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 ...

agentic systems and the AIOps tooling behind them, ideally using AWS Bedrock AgentCore or similarExperience with managed or self-hosted ML infrastructure, such as Amazon SageMaker, feature stores (e.g. Feast) and DAG tooling (e.g. Airflow, Metaflow, or similar)Experience with model experiment tracking, ideally MLflow or Weights & Biases ...

hands-on practice in Python.Familiarity with popular machine learning libraries and frameworks such as scikit-learn, Hugging Face, PyTorch and TensorFlow.Experience with MLFlow, AWS SageMaker and Bedrock is preferred.Familiar with MLOps concept, SQL and PySparkExperience with feature stores, machine learning models as service, and monitoring dashboards is a plus.Some ...

problem statements and figure out how to solve themSome extra skills that are great (but not essential): Experience with MLOps tools: Airflow, MLflow, AWS SageMaker, AWS S3, AWS EMR, CI/CDPrior experience in the fraud domain and a strong understanding of fraud detection techniquesExperience designing and deploying ...

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 ...