19 of 19 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 ...

resilient and 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 ...

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 ...

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 ...

engineering agentic AI systems • Knowledge of the healthcare/pharma domain and experience applying AI to healthcare data • Experience with AWS, especially ECS, Bedrock, SageMaker, serverless compute, and storage • Ability to prototype proof-of-concept web applications with familiarity across the full stack • Expert usage of AI coding tools ...

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 ...

Pods environments .Manage container lifecycle with ECR, private image builds, and integration with Artifactor yand external repositories .Support AI/ML platforms by configuring SageMaker Studio, Jupyter environments, an dintegrating compute layers (HyperPods) .Ensure observability, cost optimization (FinOps),vulnerability remediation, and end-to-en dsystem testing . ...

Pods environments .Manage container lifecycle with ECR, private image builds, and integration with Artifactor yand external repositories .Support AI/ML platforms by configuring SageMaker Studio, Jupyter environments, an dintegrating compute layers (HyperPods) .Ensure observability, cost optimization (FinOps),vulnerability remediation, and end-to-en dsystem testing . ...

Pods environments .Manage container lifecycle with ECR, private image builds, and integration with Artifactor yand external repositories .Support AI/ML platforms by configuring SageMaker Studio, Jupyter environments, an dintegrating compute layers (HyperPods) .Ensure observability, cost optimization (FinOps),vulnerability remediation, and end-to-en dsystem testing . ...

Pods environments .Manage container lifecycle with ECR, private image builds, and integration with Artifactor yand external repositories .Support AI/ML platforms by configuring SageMaker Studio, Jupyter environments, an dintegrating compute layers (HyperPods) .Ensure observability, cost optimization (FinOps),vulnerability remediation, and end-to-en dsystem testing . ...

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 ...

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 ...

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 ...

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 ...

Role Summary We are seeking a Principal Machine Learning Engineer (SageMaker, MLOps, Model Governance & Explainability) to provide technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that … platform, including data pipelines, model training workflows, inference runtimes, and telemetry ecosystems. Lead adoption of best‐in‐class MLOps patterns, platform tooling, and AWS SageMaker capabilities across training, processing, registry, monitoring, and deployment. Partner with platform, security, and data engineering teams to implement scalable data lakehouse oriented feature architectures ...