126 to 150 of 448 Remote Amazon SageMaker Jobs

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

business. Tech-savvy : Proficient in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX). Strong command of cloud-native 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 More ❯

machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. Bachelor's degree (master's or higher strongly preferred) in machine learning, computer science, data science, applied More ❯

deep learning, computer vision, natural language processing, etc Experience in building ML models in production using AWS ecosystem, especially ML related services such as SageMaker Ability to work independently and collaboratively with multi-functional teams with excellent communication and presentation skill Experience in writing unit tests and documentation for More ❯

the gap between technical and non-technical teams. Good command of analytical programming and visualization libraries (e.g., R, Matplotlib, ggplot) and supporting tools (e.g., Sagemaker, VS Code). Job Benefits 25 days paid holiday in addition to bank holidays to relax and refresh throughout the year. Hybrid Working: We More ❯

on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (, AWS Sagemaker, Azure Machine Learning studio) Be a ‘lifelong learner’ and can demonstrate a drive to always be learning and developing your skillsets and develop the More ❯

tools and technologies, such as Helm, Docker, Terraform and CI/CD pipelines (GitHub Actions). Knowledge of MLOps especially on cloud environments: Vertex, Sagemaker, Synapse, is a huge plus. Strong Knowledge of the software development lifecycle (code review, version control, tooling, testing, etc.). Understanding of the full More ❯

machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. • Bachelor’s degree (master’s or higher strongly preferred) in machine learning, computer science, data science, applied More ❯

or ML solutions in client-facing or consulting settings Strong programming skills in Python and familiarity with AWS services such as S3, Lambda, and SageMaker Experience with Generative AI Models , including building, fine-tuning, and deploying generative models Deep understanding of Natural Language Processing (NLP) techniques like transformer models More ❯

business areas, including marketing, sales, claims, retention, customer behavior, fraud detection, and customer servicing. Deploy machine learning models into production using AWS services, including SageMaker, S3, Feature Store, ensuring scalable, reliable, and monitored solutions that directly support key business processes. Collaborate with product management and engineering teams to integrate More ❯

business areas, including marketing, sales, claims, retention, customer behavior, fraud detection, and customer servicing. Deploy machine learning models into production using AWS services, including SageMaker, S3, Feature Store, ensuring scalable, reliable, and monitored solutions that directly support key business processes. Collaborate with product management and engineering teams to integrate More ❯

as well as contribute to it Strong software engineering, systems architecture, and unit testing capabilities and experience using pipeline tools such as Apache Airflow, Amazon SageMaker, or similar Familiarity with SQL and experience with AWS or other cloud providers Experience with GitLab CI/CD pipelines for automated More ❯

as well as contribute to it Strong software engineering, systems architecture, and unit testing capabilities and experience using pipeline tools such as Apache Airflow, Amazon SageMaker, or similar Familiarity with SQL and experience with AWS or other cloud providers Experience with GitLab CI/CD pipelines for automated More ❯

Experience across cloud platforms (AWS, Azure, GCP), with DevOps/ML Ops tools (e.g. Terraform, Docker, Kubernetes) Expertise with data science platforms like Databricks, SageMaker, or AzureML Excellent stakeholder engagement and communication skills Proven experience managing teams and delivering complex projects Security Requirements You must be eligible for Developed More ❯

across cloud platforms (AWS, Azure, GCP), with DevOps/ML Ops tools (e.g., Terraform, Docker, Kubernetes). Expertise with data science platforms like Databricks, SageMaker, or AzureML. Excellent stakeholder engagement and communication skills. Proven experience managing teams and delivering complex projects. Security Requirements You must be eligible for Developed More ❯

of embeddings, vector databases (Pinecone, Weaviate, FAISS), and RAG pipelines NLP Fundamentals : Text preprocessing, language modelling, and semantic similarity Cloud Experience : AWS ecosystem knowledge (SageMaker, Lambda, etc.) Production Ready : ETL pipelines, version control, Agile methodologies It would be a major advantage if you have experience with: Experience with 3D More ❯