8 of 8 Part-Time Amazon SageMaker Jobs in the UK

/CD pipelines, model versioning, and monitoring. \n Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. \n Proficiency in using containerization technologies (e.g., Docker ...

/CD pipelines, model versioning, and monitoring. \n Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools such as SageMaker, Vertex AI, or Azure ML Studio. \n Working knowledge of containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) to build and manage scalable ...

including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud data and AI platforms ...

frameworks (AutoPrompt, DSPy) and building evaluation suites. \n Hands-on experience with agentic frameworks (LangChain, LangGraph). \n Experience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. \n Demonstrated experience in financial services, particularly investment banking operations. \n\n Join us to help shape the future ...

very strong software‐engineering background with clear motivation to grow into ML and MLOps \n\n Desirable skills (strong differentiators): \n \n Using AWS SageMaker for training, deploying, and operating machine‐learning workloads, or demonstrating equivalent experience on similar cloud ML platforms \n Exposing machine‐learning models via APIs ...

skills \n \n Product management experience for AI/ML, MLOps, or data platforms. \n Familiarity with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores. \n Familiarity with AI concepts, such as Retrieval-Augmented Generation (RAG), agentic system architectures, evaluation of LLMs and agents … Model Context Protocol (MCP). \n Hands-on experience with or solid understanding of Amazon Web Services (AWS). \n Understanding of cloud architecture and the software development lifecycle. \n Experience coaching and mentoring other product managers and setting a high bar for execution. \n Experience operating at scale ...

experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning studio) \n Be a ‘lifelong learner’ and can demonstrate a drive to always be learning and developing your skillsets and develop ...

metadata, automated quality profiling, and governance guardrails. \n Consumers: Reduce friction from discovery to access - self-service provisioning, entitlement workflows, one-click integration with SageMaker, Databricks, and EMR. \n Network effects: Analyze usage trends to improve data quality, discovery and relevancy across persona groups \n\n Collaborate with Engineering ...