1 to 25 of 33 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 ❯

science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to 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 ❯

science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to More ❯

developing and deploying ML models in production environments. Strong Python skills, with experience in frameworks like PyTorch, TensorFlow, or Hugging Face. Confident working in AWS or similar cloud environments (SageMaker, Lambda, Docker, etc.). Experienced in (or eager to explore) areas such as forecasting, optimisation, reinforcement learning, generative AI, or computer vision. Solid engineering mindset, you know how to 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 ❯

Ability to communicate complex analytical concepts clearly and effectively to a range of audiences. Experience with model governance, documentation, and deployment best practices. Experience with cloud environments (e.g., AWS Sagemaker). Experience with collaborative development tools (e.g., Git, JIRA) is a plus. Prior experience in financial services, banking, or credit risk modelling is beneficial. 5-8 years experience in 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 ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Dfferent Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

of the curve in Generative AI, ML infrastructure, and cloud-native tooling. Tech Stack Programming: Python (Java familiarity is a plus). AWS: S3, Kinesis, Glue, Lambda, Step Functions, SageMaker, and more. On-Prem: Managed Kubernetes Platform and Hadoop ecosystem. Why This Role is Different Direct Impact: Build AI tools that traders and quants use daily to optimize strategies. More ❯

AI solutions are ethical, scalable, and reliable in production. Your skillset Proven experience designing and scaling enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep More ❯

AI solutions are ethical, scalable, and reliable in production. Your skillset Proven experience designing and scaling enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep More ❯

AI solutions are ethical, scalable, and reliable in production. Your skillset Proven experience designing and scaling enterprise-grade AI/ML systems. Expertise with cloud AI stacks (AWS Bedrock, SageMaker, Azure OpenAI, Azure ML). Strong skills in Python, FastAPI, CI/CD, MLOps, and LLM orchestration. Leadership experience across multidisciplinary AI teams. Strategic mindset — able to bridge deep 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 ❯

real users. Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows. Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems. Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure). Experience with relational databases and More ❯

experience. Strong backend development skills. Proven experience with containerization (ECS, Fargate, Docker, Kubernetes). Experience with LLMs, vector databases, prompt engineering, and multi - agent frameworks, and distributed training on SageMaker, SLM fine-tuning. Strong experience with AWS, including deploying and managing cloud-native applications. Excellent communication and stakeholder engagement skills. Ability to work in ambiguous, fast-moving environments with … minimal oversight. Experience in financial services or regulated industries. Familiarity with AWS AI/ML toolsets such as Amazon Bedrock, SageMaker, Jumpstart, POD, Kendra, and Lambda. Exposure to DevOps tools and practices such as Terraform, Ansible, and CI/CD pipelines. Understanding of data privacy, compliance, and security in AI systems." Design, develop, and deploy containerized agent-based More ❯

the judgement to apply the right approach Previous experience developing real-world ML products in areas like demand forecasting, optimisation, or computer vision Comfortable working with AWS services (e.g. SageMaker, Lambda) Strong grasp of statistics and modelling, with the ability to turn raw data into actionable insight Willingness to learn the basics of backend development (Python/Django or 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 ❯