4 of 4 Permanent Amazon SageMaker Jobs in Bolton

APIs, and services used globally. Develop in one or more core technology areas: NoSQL (MongoDB, DocumentDB) Graph databases (GraphDB, Neo4j) Search technologies (ElasticSearch, OpenSearch, SOLR) ML/AI frameworks (SageMaker, Bedrock) Cloud-based data engineering (AWS) Build and maintain cloud-native data pipelines, ensuring data quality, accuracy, and reliability. What We’re Looking For 5+ years’ experience in software 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 ❯