51 to 75 of 366 Amazon SageMaker Jobs in the UK

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

Requirements: Experienced Python developer Experience utilising machine learning libraries such as (in order of importance) TensorFlow, HuggingFace, scikit-learn, PyTorch, Pandas, NumPy, SciPy Experience with AWS (principally EC2, S3, SageMaker) or Azure/GCP equivalents Some experience of designing, developing and deploying scalable infrastructure (eg Apache Airflow, Luigi or other cluster management software) Object Orientated concepts and design The More ❯

lead technical position, ideally within a product-focused SaaS environment. Strong command of Python and relevant libraries for machine learning, data engineering, and automation. Experience working with AWS (e.g., SageMaker, Bedrock) and infrastructure-as-code tools like Terraform. Solid understanding of large-scale data pipelines, distributed systems, and microservice architectures. Comfortable working with LLMs, model fine-tuning, and building More ❯

product environments or a data-rich business. Tech-savvy : Proficient in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). Strong command of cloud- AI tooling (e.g., AWS SageMaker, GCP Vertex AI, AzureML/AzureAI, MLflow, Weights & Biases). Well-rounded Engineer : Comfortable working with version-controlled codebases, DevOps pipelines, containerisation/microservices (Docker/Kubernetes), and Infrastructure More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. About the company J.P. Morgan is a leader in financial services, offering More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. About Us J.P. Morgan is a global leader in financial services, providing More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. About Us J.P. Morgan is a global leader in financial services, providing More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. About Us J.P. Morgan is a global leader in financial services, providing More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. About Us J.P. Morgan is a global leader in financial services, providing More ❯

MLOps or related role, supporting a team of at least 3 ML engineers. Strong hands-on experience with Kubernetes and container orchestration for ML workloads. Experience with the AWS SageMaker suite of tools Familiarity with modern data labeling and management platforms (e.g., Label Studio, Kili, Scale AI, CVAT, Prodigy, Postgres, etc.). Experience designing, maintaining, and scaling ML pipelines More ❯

Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods. Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker, EKS, etc. Experience working with large-scale MLOps pipelines, working with and deploying models to production services. ABOUT US J.P. Morgan is a global leader in financial services, providing More ❯

within large organisations, through e.g. the RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning More ❯

large-scale data workflow orchestration platforms (, Airflow). Leverage expertise in cloud computing platforms (AWS and Azure) to build and optimize AI infrastructure, using services like AWS Bedrock, S3, SageMaker, Azure AI Search, etc. Champion ML governance by ensuring guidelines are followed, monitoring SLAs, and continuously improving the performance and reliability of AI solutions. Translate complex data science and More ❯

Job Title Senior Machine Learning Engineer Job Description Here at UnderwriteMe, we are on a mission to make life insurance more widely accessible and ensure people and their loved ones are protected when the inevitable happens. We are doing this More ❯

/or AWS Machine Learning Specialty Proven track record of architecting, deploying, and optimizing complex cloud solutions for AI-driven workloads. Generative AI and Cutting-Edge Technologies: Expertise in Amazon Bedrock for deploying foundation models and managing scalable GenAI workloads. Proficiency in fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker JumpStart … and Hugging Face on AWS. Experience leveraging Amazon Q (formerly AWS CodeWhisperer) Business and Developer for AI-powered coding productivity and automation. Machine Learning Frameworks and Pipelines: In-depth knowledge of Amazon SageMaker, including Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability. Skilled in distributed model training with multi-GPU clusters … and optimization for high-performance inference. Data Engineering and AI Workflow Optimization: Proficient in building data pipelines with Amazon S3, AWS Glue, Lake Formation, and Redshift for AI and ML workloads. Experienced in optimizing data preparation for large-scale AI model training and inference workflows. AI Integration and Deployment: Expertise in building end-to-end AI pipelines using AWS More ❯

Delivery Practice Manager - Data Analytics, ASEAN Professional Services The Amazon Web Services Professional Services (ProServe) team is seeking an experienced Delivery Practice Manager (DPM) to join our ProServe Shared Delivery Team (SDT) at Amazon Web Services (AWS). In this role, you'll manage a team of ProServe Delivery Consultants while supporting AWS enterprise customers through transformative projects. … to address customer outcomes. Possessing the ability to translate technical concepts into business value for customers and then talk in technical depth with teams, you will cultivate strong customer, Amazon Global Sales (AGS), and ProServe team relationships which enables exceptional business performance. DPMs success is primarily measured by consistently delivering customer engagements by supporting sales through scoping technical requirements … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯

balance cost, speed and data quality. Experimentation - set up offline metrics and online A/B tests; analyse uplift and iterate quickly. Production delivery - build scalable pipelines in AWS SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering … Product and Ops teams to translate business constraints into mathematical formulations. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) SQL (Redshift, Snowflake or similar) AWS SageMaker → Azure ML migration, with Docker, Git, Terraform, Airflow/ADF Optional extras: Spark, Databricks, Kubernetes. What you'll bring 3-5+ years building optimisation or recommendation systems at scale. Strong … know-how - LP/MIP, heuristics, constraint tuning, objective-function design. Python toolbox: pandas, NumPy, scikit-learn, PyTorch/TensorFlow; clean, tested code. Cloud ML: hands-on with AWS SageMaker plus exposure to Azure ML; Docker, Git, CI/CD, Terraform. SQL mastery for heavy-duty data wrangling and feature engineering. Experimentation chops - offline metrics, online A/B More ❯