21 of 21 Remote Amazon SageMaker Jobs in Central London

SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST, data governance, GDPR-compliant pipelines More ❯

scikit-learn, pandas, NumPy). 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, or GCP) for model deployment and orchestration. More ❯

or internal clients within large organisations) through RFI/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools More ❯

similar) Strong understanding of ML principles and model evaluation Experience with cloud-based model deployment (GCP preferred) Familiarity with containerised workflows (Docker, serverless) and MLOps tools (MLflow, Vertex AI, SageMaker) Excellent communication and collaboration skills in a remote-first team Willingness to travel occasionally for in-person collaboration or client work Nice to Have Experience with retrieval-augmented generation More ❯

decisioning is strongly preferred Experience with model governance and monitoring in regulated environments Experience with cloud platforms (AWS, GCP, Azure), preferably AWS, ML tools such as the AWS suite: Sagemaker Key Details Reporting to Lead Data Scientist Hours Full time Location London - Hybrid WFH model, x2 days a week onsite (Wed/Thurs) Working with Yaspa We are a More ❯

access control Strong understanding of security and compliance requirements in financial services (FCA, DORA, PRA) Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, or CodePipeline) Desirable exposure to SageMaker , model lifecycle integration, and MLOps monitoring Role Overview You’ll be part of a growing platform engineering function building the next generation of secure AWS services. This position suits More ❯

Proven experience in solution design, technical consulting, or pre-sales Ability to communicate complex concepts clearly to varied audiences Familiarity with cloud AI environments such as Azure AI, AWS Sagemaker, or GCP Vertex A natural problem solver with curiosity for cutting-edge tools and frameworks Desirable Experience Experience delivering AI or ML solutions in a commercial or consultancy setting More ❯

Docker, Git, cloud networking, and cloud security for services and infrastructure. Experience with Kubernetes (K8S) is a plus. - Cloud Expertise: Familiarity with AI infrastructure on AWS and GCP, including Sagemaker, Vertex, Triton, and GPU computing. - Bonus Points: Experience with Airbyte is a significant advantage. Perks and Benefits: - Hybrid/Remote Option: Freedom to work from anywhere in the world More ❯

in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track … We provide a competitive package with benefits, bonuses, and equity shares to encourage ownership in our success. Our leaders have built and sold software firms and held roles at Amazon and Microsoft. The role will be based from home so if you are looking for flexible working in an grown up environment where you are trusted and given the More ❯

in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track … We provide a competitive package with benefits, bonuses, and equity shares to encourage ownership in our success. Our leaders have built and sold software firms and held roles at Amazon and Microsoft. The role will be based from home so if you are looking for flexible working in an grown up environment where you are trusted and given the More ❯

in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track … We provide a competitive package with benefits, bonuses, and equity shares to encourage ownership in our success. Our leaders have built and sold software firms and held roles at Amazon and Microsoft. The role will be based from home so if you are looking for flexible working in an grown up environment where you are trusted and given the More ❯

spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering … or Machine Learning Engineer. A strong grounding in Python, with hands-on experience building and shipping AI/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset More ❯

spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering … or Machine Learning Engineer. A strong grounding in Python, with hands-on experience building and shipping AI/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset More ❯

spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering … or Machine Learning Engineer. A strong grounding in Python, with hands-on experience building and shipping AI/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset More ❯

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 About the Company Our client is an extremely well know, digital marketplace focused on sustainable ecommerce. With over 35 million More ❯

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 About the Company Our client is an extremely well know, digital marketplace focused on sustainable ecommerce. With over 35 million More ❯

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 About the Company Our client is an extremely well know, digital marketplace focused on sustainable ecommerce. With over 35 million More ❯

Data Scientist Hybrid Working – Local Site – 1-2 days a week on site. Financial Services Lorien's leading banking client is looking for a number of Data Scientists to join them on a new long term project which will be More ❯

the intersection of machine learning and cloud infrastructure, ensuring our models run efficiently, reliably, and cost-effectively at scale. Key Responsibilities Design, deploy, and manage ML infrastructure on AWS (SageMaker) and Kubernetes Build scalable pipelines for LLM and video generation models Optimise model inference, latency, and costs Implement monitoring, logging, and alerting for production systems Collaborate with ML teams … automation and tooling for smoother ML operations Skills & Experience Strong Python skills and production coding experience Proven experience deploying/scaling LLMs or generative AI models Expertise in AWS SageMaker, Kubernetes, and CI/CD (Terraform/CloudFormation) Solid understanding of ML serving, optimisation, and cloud architecture Nice to Have: Video generation or computer vision experience, model compression, distributed More ❯

the intersection of machine learning and cloud infrastructure, ensuring our models run efficiently, reliably, and cost-effectively at scale. Key Responsibilities Design, deploy, and manage ML infrastructure on AWS (SageMaker) and Kubernetes Build scalable pipelines for LLM and video generation models Optimise model inference, latency, and costs Implement monitoring, logging, and alerting for production systems Collaborate with ML teams … automation and tooling for smoother ML operations Skills & Experience Strong Python skills and production coding experience Proven experience deploying/scaling LLMs or generative AI models Expertise in AWS SageMaker, Kubernetes, and CI/CD (Terraform/CloudFormation) Solid understanding of ML serving, optimisation, and cloud architecture Nice to Have: Video generation or computer vision experience, model compression, distributed More ❯

the intersection of machine learning and cloud infrastructure, ensuring our models run efficiently, reliably, and cost-effectively at scale. Key Responsibilities Design, deploy, and manage ML infrastructure on AWS (SageMaker) and Kubernetes Build scalable pipelines for LLM and video generation models Optimise model inference, latency, and costs Implement monitoring, logging, and alerting for production systems Collaborate with ML teams … automation and tooling for smoother ML operations Skills & Experience Strong Python skills and production coding experience Proven experience deploying/scaling LLMs or generative AI models Expertise in AWS SageMaker, Kubernetes, and CI/CD (Terraform/CloudFormation) Solid understanding of ML serving, optimisation, and cloud architecture Nice to Have: Video generation or computer vision experience, model compression, distributed More ❯