5 of 5 Amazon SageMaker Jobs in Yorkshire

AWS S3 Data Lake using VS Code, Glue, Lambda, Git Hub and Terraform Experience with/understanding of a wide variety of analytics tools such as SQL, Alteryx, AWS Sagemaker, Quicksight, Tableau and PowerBI Desirable: Experience using Lake Formation Experience with AWS Kinesis/Firehose for real-time data pipelines Experience of rail, airline or leisure industry Experience with More ❯

engineers. This role will require the software and dev ops capabilities of an ML Ops engineer to build ML and CI-CD pipelines, proficiency in Infrastructure as Code and Sagemaker pipelines combined with Data Engineering capabilities. Role Summary: Working across projects within the business and the wider industry through the organisation's parent company, you will have responsibility for … and scripting in Bash and Python. Strong experience with AWS infrastructure with proficiency in the services such as: S3, EC2, Lambda functions, Cloud Formation, Athena, Dynamo DB, Code Commit, SageMaker, etc. Strong experience with containerisation using Docker and containers management. Strong software engineering skillset including code review: a good understanding of coding best practices and experience with code and More ❯

in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations while supporting growth. Finally, you will track … We provide a competitive package with benefits, bonuses, and equity shares to encourage ownership in our success. Our leaders have built and sold software firms and held roles at Amazon and Microsoft. The role will be based from home so if you are looking for flexible working in an grown up environment where you are trusted and given the More ❯

spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain, PyTorch, and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker, or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI/CD and model monitoring to cost and performance optimisation. Partnering … or Machine Learning Engineer. A strong grounding in Python, with hands-on experience building and shipping AI/ML or LLM-driven products. Experience with cloud ML platforms (AWS SageMaker, Bedrock, Vertex AI, etc.). Familiarity with LangChain or similar orchestration frameworks. A solid understanding of MLOps and how to take models from concept to production. A collaborative mindset More ❯

the intersection of machine learning and cloud infrastructure, ensuring our models run efficiently, reliably, and cost-effectively at scale. Key Responsibilities Design, deploy, and manage ML infrastructure on AWS (SageMaker) and Kubernetes Build scalable pipelines for LLM and video generation models Optimise model inference, latency, and costs Implement monitoring, logging, and alerting for production systems Collaborate with ML teams … automation and tooling for smoother ML operations Skills & Experience Strong Python skills and production coding experience Proven experience deploying/scaling LLMs or generative AI models Expertise in AWS SageMaker, Kubernetes, and CI/CD (Terraform/CloudFormation) Solid understanding of ML serving, optimisation, and cloud architecture Nice to Have: Video generation or computer vision experience, model compression, distributed More ❯