1 to 25 of 53 Permanent Amazon SageMaker Jobs in London

that fuel analytics, AI/ML, and data-driven operations.* Design and build high-performance data integration solutions: Use AWS Glue, AWS Step Functions, Amazon EventBridge, and AWS Database Migration Service (DMS) to orchestrate ingestion, transformation, and movement of data across on-premises and cloud sources into Amazon … Design and oversee the delivery of secure lakehouse and warehouse architectures: Implement logical and physical data models, data quality controls, and governance practices using Amazon S3 (data lake), AWS Lake Formation, Amazon Redshift, and Amazon Athena to ensure trusted data.* Ability to design AI/ML-enabled ...

based programming languages & good working knowledge of python. Experience building and operating data pipelines and analytical systems using technologies such as Google BigQuery, Amazon Athena, or ClickHouse. Experience with distributed data processing frameworks such as Apache Spark and/or Apache Flink. Experience with messaging and streaming systems such … Exposure to agent-based architectures and emerging protocols such as the Model Context Protocol (MCP). Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) #ICBCareer #ICBEngineering ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice ...

based programming languages & good working knowledge of python. Experience building and operating data pipelines and analytical systems using technologies such as Google BigQuery, Amazon Athena, or ClickHouse. Experience with distributed data processing frameworks such as Apache Spark and/or Apache Flink. Experience with messaging and streaming systems such … Exposure to agent-based architectures and emerging protocols such as the Model Context Protocol (MCP). Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) #J-18808-Ljbffr ...

Docker, Kubernetes) to operate, support, and secure mission-critical applications Preferred Qualifications, Capabilities and Skills Experience with MLOps tools and platforms such as MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols ...

RFPs/tenders; experience running hands‐on product demos independently. Strong knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML services (e.g., SageMaker, Vertex AI, AzureML), including sovereign, on‐premise, and hybrid deployment models. Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure or GCP; experienced with containerisation, IaC, event ...

serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI). Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design, vector databases (Pinecone, Milvus, Weaviate), and agentic ...

functional, and globally distributed teams. Continuous learning mindset with a focus on emerging technologies and innovation. 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 ...

training,evaluationand production deployment, includingMLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn,PyTorch,XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering Architecture and delivery of scalable cloud data and AI platforms on AWS,Azureor GCP; experienced with ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

. Hands‐on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry ...

. Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry ...

using Docker and Kubernetes. Engineering: Strong software engineering fundamentals, including version control, testing, and API/microservices architecture. Experience with MLOps platforms (e.g., MLflow, SageMaker) or agentic AI frameworks. Familiarity with secure development practices or regulated/high-compliance environments. Background in NLP, computer vision, or data engineering platforms. ...

skills in Python, with experience building scalable services, APIs, and production-quality code Experience with modern ML tooling and platforms (e.g.Databricks,MLflow, Airflow, Kubeflow, SageMaker, Vertex AI) Familiarity with CI/CD pipelines and infrastructure-as-code (e.g.Terraform, CloudFormation) Experience building robust, maintainable, and testable ML pipelines and APIs ...

Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines). Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI) Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

preferred). Hands-on experience with GCP Vertex AI(model endpoints, pipelines, embeddings, vector search)or equivalent cloud-nativeAI/ML platforms (e.g. AWS SageMaker, AzureML) andagentorchestration frameworks(e.g.LangChain,LangGraph, ADK). Solid understanding ofMLOps - CI/CD,IaC(Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll do Partner with stakeholders across the business to explore high‐impact opportunities. Own the full lifecycle: problem framing, data discovery … building RAG systems. Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability. Clear communication with ...

video, audio) ML pipelines.* Proficiency in Python and common AI/ML libraries (e.g., PyTorch, Hugging Face Transformers, LangChain) and cloud frameworks (VertexAI, Bedrock, Sagemaker).* Strong proficiency in Git, CI/CD workflows, and containerization (Docker/Kubernetes).* Proficiency with Linux environments and cloud platforms (e.g. ...

balance technical depth with speed and business impact. You’ll really thrive in this role if you have: Proven experience with AWS, including SageMaker, Bedrock, Lambda, S3, and Redshift. Familiarity with MLOps best practices: evaluation, deployment, monitoring, and iterative improvement. Experience in Adtec, Media, or fraud detection. Hands ...

Experience Proven experience designing and deploying production-grade AI/ML or LLM systems . Expertise with AWS or Azure AI services (e.g., Bedrock, SageMaker, OpenAI, Cognitive Services). Strong Python programming skills with hands‐on experience using frameworks like Autogen and LangGraph . Solid grounding in MLOps , containerisation ...

training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMaker* Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands‐on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML). Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

Bedrock Experience with Kubernetes, CI or CD, infrastructure as code, and automation tools Familiarity with AI or ML platforms such as Databricks, SageMaker, or MLflow Exposure to hybrid cloud architectures and large scale data processing ecosystems Knowledge of enterprise data platforms, observability, and cost management tooling Leadership Expectations ...

documentation and collaborate across engineering teams. Ecosystem Knowledge : Hands-on experience supporting software delivery and MLOps tooling (e.g., Kubernetes, Kubeflow, MLflow, Triton, Docker, or SageMaker/Azure ML) Equal Opportunity Statement We are committed to fostering and promoting an inclusive professional environment for all of our employees ...

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