1 to 25 of 30 Permanent Amazon SageMaker Jobs in London

Data Pipeline Engineering Vector Databases such as Pinecone, Weaviate or ChromaDB Cloud & DevOps Experience with at least one major cloud platform: AWS (preferred) Bedrock SageMaker Lambda ECS/EKS S3 Or experience within: Microsoft Azure Google Cloud Platform (GCP) Additional experience with: Docker Kubernetes Git CI/CD Pipelines ...

operate, support, and secure mission-critical applications\ NPreferred qualifications, capabilities and skills\ N\n\nExperience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow\ N\n\nExposureto cloud-native microservices architecture\ N\n\nFamiliarity with advanced AI/ML concepts ...

distributed teams Continuous learning mindset with a focus on emerging technologies and innovation Nice-to-Have Experience with AWS or GCP ML platforms (e.g., SageMaker, Vertex AI) Front‐end development (React) or backend development (.NET/C#) Commercial awareness and understanding of business value delivery #J-18808-Ljbffr ...

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 ...

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 ...

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 ...

deployment, including MLOps, monitoring and drift detection. \ N\n\nPractical knowledge of ML frameworks (e.G. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \ N\nPlatform & Cloud Engineering: \ N\nArchitecture and delivery of scalable cloud data and AI platforms on AWS, Azure ...

tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation, A/B testing and online evaluation ...

Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication and collaboration skills, with the ability to translate complex technical concepts for diverse, cross-functional stakeholders. ...

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 ...

Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security, and scaling. Experience integrating human-in-the-loop or user feedback signals into ...

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. ...

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 ...

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 ...

prompt optimization frameworks (AutoPrompt, DSPy) and building evaluation suites.\ NHands-on experience with agentic frameworks (LangChain, LangGraph).\ NExperience with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.\ NDemonstrated experience in financial services, particularly investment banking operations.\ N\nJoin us to help shape the future of Market Operations ...

DSPy) and building evaluation suites.\ N\n\nExperience with distributed computing, data sharding, and performance optimization.\ N\n\nHands-onwith AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration.\ N\n\nDemonstrated experience in financial services, particularly investment banking operations.\ N\n\nABOUT USJ.P. Morgan is a global leader ...

qualifications, capabilities & skills\ N\nProduct management experience for AI/ML, MLOps, or data platforms.\ NFamiliarity with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores.\ NFamiliarity with AI concepts, such as Retrieval-Augmented Generation (RAG), agentic system architectures, evaluation of LLMs and agents … Model Context Protocol (MCP).\ NHands-on experience with or solid understanding of Amazon Web Services (AWS).\ NUnderstanding of cloud architecture and the software development lifecycle.\ NExperience coaching and mentoring other product managers and setting a high bar for execution.\ NExperience operatingat scale in highly available, secure environments. ...

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 ...

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 ...

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 ...

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)\ NBe a ‘lifelong learner’ and can demonstrate a drive to always be learning and developing your skillsets and develop ...

Tungsten Automation solutions Production AI Experience : Leverage experience with deploying, monitoring, and maintaining AI/LLM models in production (e.g., Azure ML, AWS Sagemaker, or Kubernetes-based ML pipelines) Thought Leadership : Provide thought leadership on AI-driven automation trends and best practices, both internally and externally Collaboration and Cross ...

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 ...

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 ...