1 to 25 of 38 Remote Permanent Amazon SageMaker Jobs

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 ❯

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 ❯

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 ❯

monitoring, and performance optimisation of ML systems Contribute to architectural discussions and promote engineering best practices Technical Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track 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 ❯

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 ❯

production environments. Set up, integrate, and maintain a scalable, stable set of CI/CD tools to support development, testing, and security scanning. Implement Amazon CloudWatch, Splunk and other third party monitoring solutions to provide continuous monitoring capabilities, track all aspects of the system, infrastructure, performance, application errors and … procedures such as NIST, FISMA, FedRamp and Common Criteria regulations and standards. Familiarity with the MLOps, machine learning lifecycle and product landscape, for example: Amazon SageMaker, Apache Airflow, Looker, Trifacta etc. You don't need to be an expert in all these. Working knowledge of Linux Professional Skills More ❯

data analysts, engineers, and architects to improve model performance and integrate insights. The successful candidate will: Have proficiency with AWS cloud services such as SageMaker, Lambda, S3, Step Functions, EMR, and Glue. Understand the machine learning development cycle, including data engineering, analysis, modeling, and operations. Design and implement scalable More ❯

and LLM frameworks. End-to-End AI Delivery Design, build, and maintain scalable AI and LLM-based pipelines using AWS or Azure services (e.g., SageMaker, Azure ML, Databricks, OpenAI integrations). Oversee AI model lifecycles from data preprocessing and prompt engineering through to deployment and continuous monitoring in production More ❯

and LLM frameworks. End-to-End AI Delivery Design, build, and maintain scalable AI and LLM-based pipelines using AWS or Azure services (e.g., SageMaker, Azure ML, Databricks, OpenAI integrations). Oversee AI model lifecycles from data preprocessing and prompt engineering through to deployment and continuous monitoring in production 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 ❯

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 ❯

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 ❯

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 ❯

and experience of Data Governance frameworks and implementation. Exposure to CI/CD pipelines and source control for reports and analytics. Bonus: Familiarity with SageMaker or similar ML/predictive analytics platforms. We're looking for someone who: Thinks like a data user, not just a data builder – with More ❯

and experience of Data Governance frameworks and implementation. Exposure to CI/CD pipelines and source control for reports and analytics. Bonus: Familiarity with SageMaker or similar ML/predictive analytics platforms. We're looking for someone who: Thinks like a data user, not just a data builder – with More ❯

and experience of Data Governance frameworks and implementation. Exposure to CI/CD pipelines and source control for reports and analytics. Bonus: Familiarity with SageMaker or similar ML/predictive analytics platforms. We're looking for someone who: Thinks like a data user, not just a data builder with More ❯

and experience of Data Governance frameworks and implementation. Exposure to CI/CD pipelines and source control for reports and analytics. Bonus: Familiarity with SageMaker or similar ML/predictive analytics platforms. We're looking for someone who: Thinks like a data user, not just a data builder with More ❯

and experience of Data Governance frameworks and implementation. Exposure to CI/CD pipelines and source control for reports and analytics. Bonus: Familiarity with SageMaker or similar ML/predictive analytics platforms. We're looking for someone who: Thinks like a data user, not just a data builder with More ❯

cloud security for services and infrastructure. Experience with Kubernetes (K8S) is a plus. Cloud Expertise: Familiarity with AI infrastructure on AWS and GCP, including Sagemaker, Vertex, Triton, and GPU computing. LLM Deployment: Experience with local (cloud) deployment of OpenSource LLM, like LLAMA, DeepSeek. Bonus Points: Experience with Airbyte and More ❯