14 of 14 Amazon SageMaker Jobs in the City of London

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 ❯

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 ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you’ve built and scaled similar More ❯

Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you’ve built and scaled similar More ❯

related field Experience in training and deploying LLMs at scale Familiarity with cloud infrastructure and distributed computing environments Exposure to modern ML tooling such as Modal, Weights & Biases, or Amazon SageMaker Knowledge of fine-tuning techniques including LoRA, QLoRA, or other parameter-efficient frameworks Role Overview The successful candidate will be responsible for designing and implementing machine learning More ❯

Desirable experience: Familiar with Terraform, from a developer perspective. Previous work in financial services, healthcare, or similarly regulated data environments. Experience with LLMs, voice agents, or tools like AWS SageMaker, AWS Bedrock, or ElevenLabs. Exposure to soft real-time systems or distributed system architectures. Knowledge of OAuth, SQS, or running containers in production. More ❯

Desirable experience: Familiar with Terraform, from a developer perspective. Previous work in financial services, healthcare, or similarly regulated data environments. Experience with LLMs, voice agents, or tools like AWS SageMaker, AWS Bedrock, or ElevenLabs. Exposure to soft real-time systems or distributed system architectures. Knowledge of OAuth, SQS, or running containers in production. 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 ❯

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 ❯

LangGraph, LlamaIndex Experience with Hugging Face and LoRA/QLoRA for fine-tuning Experience with RAG & Vector DBs eg. FAISS, Weaviate, Pinecone Any experience of MLOps with MLFlow, AWS (SageMaker), CI/CD (GitHub Actions) or similar would be a benefit to an application The employer is well known not only for the forward-thinking approach they have to 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 ❯

Senior Data Engineer MUST HAVE SNOWFLAKE Salary - £90-100k with 15% bonus Hybrid working - couple of days in the office City of London We are looking for: Good understanding of data engineering principles A good technical grasp of Snowflake More ❯