1 to 25 of 37 Remote Permanent Amazon SageMaker Jobs

of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system design More ❯

Science or related fields. At least 2 years of experience in machine learning and data science and analytics. Proficiency in using JupyterLab or AWS Sagemaker for interactive data analysis and model development. Strong proficiency in Python and experience with libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy and More ❯

passion for machine learning and investing independent time towards learning, researching, and experimenting with new innovations in the field. Experience working with technologies like SageMaker, Athena/Trino, Spark, Milvus, and OpenSearch. At Rakuten Viber, we connect people-no matter who they are, or where they are from. As More ❯

e.g., 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. More ❯

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 More ❯

Engineers focusing on their growth and execution excellence. Lead and be responsible for on-time, great-quality project deliveries Developing MLOps pipelines leveraging the Amazon SageMaker platform and its features Developing Generative AI solutions and POCs, utilizing the latest architectures and technologies Delivering end-to-end ML products … data scientists and experience with software development and test automation Experience with one or more large language models, such as frameworks like OpenAI SDK, Amazon Bedrock, LangChain, and LlamaIndex Experience with Docker and Kubernetes Fluent written and verbal communication skills in English Working knowledge of some Vector Databases such More ❯

a programming challenge during the interview process. Experience with Jupyter Notebooks, Python Data Science Libraries. Experience with ML-OPS and related tools (e.g., MLFLOW, Sagemaker, Bedrock) and ability to build interactive, insightful dashboards for monitoring ML models. Place of Performance: Hybrid work in San Antonio, TX. Desired Skills (Optional More ❯

be fluent in Python - writing readable, testable, and maintainable code comes naturally to you. You'll be comfortable with cloud-based tools like AWS SageMaker and Lambda, and you'll know your way around Git, SQL, and common Python data science libraries (like pandas/polars, scikit-learn, xgboost More ❯

and unsupervised learning techniques Experience in demand prediction, optimisation, or computer vision is advantageous Comfortable working with cloud platforms (preferably AWS) and services like SageMaker or Lambda Strong mathematical and statistical foundations, with a sharp eye for patterns and insights Willingness to build basic backend development skills (Python/ More ❯

such as Power BI and OAS. Understanding of data replication tools such as Oracle GoldenGate and AWS DMS. Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage. Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

such as Power BI and OAS Understanding of data replication tools such as Oracle GoldenGate and AWS DMS Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

such as Power BI and OAS Understanding of data replication tools such as Oracle GoldenGate and AWS DMS Knowledge of AWS Services such as SageMaker, Lambda, Step Functions, S3 etc would be an advantage Hands on and technical understanding of complex Data Warehousing terminology. Python scripting knowledge would be More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

business in their Data Science, AI, and ML initiatives. Our Data & Analytics technology stack consists primarily of: Oracle tools, Snowflake, Postgres, various AWS Services (SageMaker, Lambda, Step Functions, DMS, S3 etc.) in the AWS Cloud. We are currently engaged on multiple data focused projects which are in various stages More ❯

down complex challenges, devise practical solutions, and iterate quickly based on feedback or data. Hands-on experience with cloud platforms, including AWS (e.g., EC2, Sagemaker, Bedrock, S3, Lambda), Google Cloud (e.g., Compute Engine, Vertex, GKE, BigQuery), Azure (e.g., Virtual Machines, Azure OpenAI) etc. Proficiency in scripting or programming (e.g. More ❯

Associate AWS Certified IoT Specialty ( Highly desirable ) Familiarity with CI/CD , DevOps , and Infrastructure as Code (Terraform or CloudFormation). Experience with AWS SageMaker or other ML/AI tools for IoT analytics. If this role is of interest - please apply now. More ❯

data versioning, quality management, and CI/CD pipelines Experience with cloud platforms (e.g., AWS or Azure) and data tools such as Terraform or SageMaker is a plus Ideally, some hands-on experience building and maintaining data pipelines in a production environment What's on Offer: Competitive salary More ❯

FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering Experience with cloud More ❯

FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering Experience with cloud More ❯

FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering Experience with cloud More ❯

FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering Experience with cloud More ❯

FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering Experience with cloud More ❯

analytical person with a bias for action. What Technologies We Use: Python, dbtLabs, SQL, GitHub Actions (CI/CD) Terraform, Docker, MLOps Platform (VertexAI, Sagemaker and MLFlow) With ecobee, you'll have the opportunity to: Be part of something big: Get to work in a fresh, dynamic, and ever More ❯

datasets. Optimize embeddings for semantic search and document retrieval tasks. Model Deployment & Optimization: Deploy ML models in production environments using cloud platforms like AWS SageMaker, ECS or equivalent tools. Ensure scalability, reliability, and low latency in production systems while monitoring model performance. Implement CI/CD pipelines for ML … Opensearch, Pinecone, Weaviate). API Development: FastAPI, Flask, Django Containerization: Docker, AWS ECS, Kubernetes Cloud & Data Tools: Experience with cloud platforms such as AWS (SageMaker preferred), GCP Vertex AI, or Azure ML for deploying ML models. Familiarity with SQL or NoSQL databases for data extraction and preprocessing tasks. Problem More ❯

Keras) Experience with NLP and computer vision models Strong understanding of machine learning algorithms and statistical modeling Experience with cloud-based AI platforms (AWS SageMaker, Google Cloud AI Platform) Excellent problem-solving and communication skills More ❯