1 to 25 of 26 Permanent Amazon SageMaker Jobs in London

delivery of a production-grade machine learning platform on AWS. What you’ll be doing Design and maintain a production MLOps platform on Amazon SageMaker (Studio, Training, Pipelines, Endpoints) — including model registry, automated retraining, drift monitoring, and governance gates Lead the migration of a 12-model production suite … e.g., the CVM suite) from legacy infrastructure to SageMaker, owning parity testing methodology and sign-off Build and maintain CI/CD pipelines (CodePipeline/CodeBuild or equivalent) for automated model promotion across environments Define and enforce IAM least-privilege policies, KMS key management, and VPC/PrivateLink network ...

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 ...

cost-effective deployment via Docker , Kubernetes (AKS/EKS/GKE) , and Terraform/Bicep . Lead enterprise MLOps initiatives using Azure ML , SageMaker , Vertex AI , MLflow , and Kubeflow , with continuous integration pipelines (GitHub Actions, Azure DevOps, Jenkins, Argo CD). Oversee monitoring and observability using Prometheus , Grafana ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...

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 ...

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

optimization frameworks (AutoPrompt, DSPy) and building evaluation suites. Experience with distributed computing, data sharding, and performance optimization. Hands-on with AWS AI deployment services (SageMaker, Bedrock) and workflow orchestration. Demonstrated experience in financial services, particularly investment banking operations.\n ABOUT US \n J.P. Morgan is a global leader ...

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. ...

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 ...

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

just experimentation). Cloud platform expertise (AWS strongly preferred; Snowflake a plus) with deep understanding of services for compute, orchestration, storage and ML (e.g., SageMaker, Lambda, ECS/EKS, Step Functions). Experience with MLOps tooling such as: Experiment tracking and model registries (MLflow, Weights & Biases, SageMaker Model … Registry) Workflow orchestration (Airflow, Prefect, Step Functions) Model serving frameworks (SageMaker, TorchServe, TensorFlow Serving, or similar) Feature stores (Feast, Tecton, or custom‐built) Deep understanding of monitoring and observability for ML systems, including operational metrics, data quality checks, drift detection and model performance tracking. CI/CD and Infrastructure ...

with the Data Engineering and AI Platform teams, you will drive excellence and pace in the build, deployment, testing, and monitoring of models using Amazon Sagemaker [and occasionally Snowflake] Causal Inference & Decision Science: Lead the development of advanced causal models (e.g., Media Mix Modelling, retention drivers, and simulation ...

seen as theoretical 'black boxes' You will understand the tools and systems used to deliver all model activities, including the DSML tools (AWS Sagemaker),Snowflake and collaborate and advocate for the right approach as the needs of the business and technology evolves You will work in collaborative relationships ...

model monitoring, content safety, LLMOps, GenAIOps or production support for AI systems Experience with non‐Microsoft AI platforms or services, such as AWS Bedrock, Amazon Q, SageMaker, Google Vertex AI, Anthropic Claude, OpenAI APIs or open‐source model ecosystems Familiarity with ISO 42001, NIST ...

provide technical leadership across the full lifecycle of machine learning systems while architecting production-grade ML solutions. You will work with AWS SageMaker, lead teams on MLOps strategies, and ensure systems adhere to governance standards. This is a hands-on, technical position ideal for those with deep expertise ...

architecture, design, and delivery of enterprise-scale generative AI solutions on AWS. Translate business problems into secure, scalable, production-grade AI systems using Amazon Bedrock, retrieval-augmented generation (RAG), agentic workflows, and cloud-native AWS services. Drive architecture standards, model orchestration, governance, observability, and operational excellence across the GenAI … home 100% of the time. Your Role: Lead architecture and implementation of GenAI applications using AWS services, especially large language model capabilities such as Amazon Bedrock Define reusable architecture patterns for model customization, prompt orchestration, retrieval pipelines, and agentic workflows Design agentic AI systems incorporating tool use, workflow orchestration ...

design authority Comfortable working directly with senior client stakeholders Strong mentoring and coaching capability Experience with some of the following technologies or ecosystems: Amazon Bedrock and SageMaker Claude/Anthropic Azure AI Foundry or Azure OpenAI Databricks Vector databases and RAG architectures Agentic AI orchestration frameworks ...