15 of 15 Permanent Amazon SageMaker Jobs in London

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 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 ...

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). ...

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 ...

Strong Generative AI architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised ...

production Strong Python development skills Hands-on experience with LangGraph, LangChain, LlamaIndex, CrewAI or similar Experience with AWS, particularly Bedrock, Lambda, ECS, EKS or SageMaker Strong understanding of RAG, agentic AI and multi-agent architectures Experience with CI/CD, cloud infrastructure and application deployment Comfortable working directly with ...

production Strong Python development skills Hands-on experience with LangGraph, LangChain, LlamaIndex, CrewAI or similar Experience with AWS, particularly Bedrock, Lambda, ECS, EKS or SageMaker Strong understanding of RAG, agentic AI and multi-agent architectures Experience with CI/CD, cloud infrastructure and application deployment Comfortable working directly with ...

offs. Real-time ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent). Cloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes. Engineering mindset: A focus on reliability, maintainability and continuous improvement. ...

engineering best practices , covering LLMOps, MLOps, CI/CD, automated evaluation, observability, security, performance and cost optimisation. Work extensively with AWS AI services , including Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions. What We Are Looking For: Proven experience as an Engineering Manager or Technical Lead with … agent frameworks such as LangGraph, Strands Agents or Claude Agent SDK, alongside interoperability standards including MCP and A2A. Strong AWS cloud engineering experience, particularly Amazon Bedrock, Bedrock AgentCore, SageMaker, Lambda and Step Functions, with Infrastructure as Code using AWS CDK or equivalent. Hands-on experience with LLMOps ...

environment evolves, but typical tools include: Python for experimentation, modelling, and evaluation Weights & Biases (or equivalent) for experiment tracking AWS , including services such as SageMaker and Bedrock Internally supported AI development platforms and tooling This role is not suited to candidates who are dogmatic about specific tools . Adaptability ...

applications. Agent orchestration, tool calling, RAG, context engineering, vector databases, event-driven integration and human approval workflows. Deployment through Anthropic APIs and/or Amazon Bedrock or Google Cloud Vertex AI, with broader AWS, Google Cloud or Microsoft Azure experience. DevSecOps, containers, infrastructure as code, cloud security, monitoring, scaling … Experience leading engineering delivery, reviewing code, mentoring developers and converting prototypes into maintainable production services. AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices ...