1 to 25 of 57 Remote Permanent Amazon SageMaker Jobs

integration challenges. You will help design and deliver high-quality, scalable features, including AI-enhanced flows, predictive monitoring tools, and automation capabilities powered by Amazon Bedrock, Amazon SageMaker, and other modern AI/ML frameworks. You’ll work cross-functionally to translate product vision into technical outcomes … sophisticated server-side and client-side features. Collaborate on the design and integration of AI/ML models into platform features, leveraging tools like Amazon Bedrock, SageMaker, and other AWS native services. Investigate and prototype the use of natural language processing, anomaly detection, and intelligent automation in the … a willingness to collaborate effectively within a team. A keen focus on improving user experience Nice to have: Experience with Integration Platforms. Familiarity with Amazon SageMaker, Amazon Bedrock, or similar ML/AI platforms (or willingness to learn quickly) Exposure to LLMs, vector databases, or embedding techniques More ❯

integration challenges. You will help design and deliver high-quality, scalable features, including AI-enhanced flows, predictive monitoring tools, and automation capabilities powered by Amazon Bedrock, Amazon SageMaker, and other modern AI/ML frameworks. You’ll work cross-functionally to translate product vision into technical outcomes … sophisticated server-side and client-side features. Collaborate on the design and integration of AI/ML models into platform features, leveraging tools like Amazon Bedrock, SageMaker, and other AWS native services. Investigate and prototype the use of natural language processing, anomaly detection, and intelligent automation in the … a willingness to collaborate effectively within a team. A keen focus on improving user experience Nice to have: Experience with Integration Platforms. Familiarity with Amazon SageMaker, Amazon Bedrock, or similar ML/AI platforms (or willingness to learn quickly) Exposure to LLMs, vector databases, or embedding techniques More ❯

Face, OpenAI APIs Agentic AI: CrewAI, AutoGPT, DSPy CRM/Data Platforms: Salesforce, HubSpot, Segment Vector DBs: Pinecone, FAISS, Weaviate Cloud: AWS (S3, Lambda, SageMaker), Azure ML Workflow: GitHub, Notion, Jira Why Join TwoSD? At TwoSD , innovation isn’t a department—it’s a mindset. Here, your voice matters More ❯

Face, OpenAI APIs Agentic AI: CrewAI, AutoGPT, DSPy CRM/Data Platforms: Salesforce, HubSpot, Segment Vector DBs: Pinecone, FAISS, Weaviate Cloud: AWS (S3, Lambda, SageMaker), Azure ML Workflow: GitHub, Notion, Jira Why Join TwoSD? At TwoSD , innovation isn’t a department—it’s a mindset. Here, your voice matters 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 ❯

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 ❯

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 ❯

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 ❯

B2B Roles & Responsibilities: We seek a skilled and proactive Senior Platform Engineer to design, build, and support scalable cloud infrastructure and automation pipelines on Amazon Web Services (AWS). You will work closely with DevOps, Security, Data, and AI Application teams to ensure platform reliability, security, and performance. Your … development, staging, and production environments. Automate operational tasks, environment provisioning, and deployments using scripting languages such as Python , Bash, or PowerShell. Enable and maintain Amazon SageMaker environments for scalable ML model training, hosting, and pipelines. Integrate AWS Bedrock to provide foundation model access for generative AI applications, ensuring … Collaborate with security and compliance teams to implement best practices around IAM, encryption, logging, monitoring, and cost optimization. Implement and manage observability tools like Amazon CloudWatch, Prometheus/Grafana, or ELK for monitoring and alerting. Support container orchestration environments using EKS (Kubernetes), ECS, or Fargate. Contribute to incident response 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 ❯

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 ❯

ML models that support commercial analytics, customer segmentation, forecasting, market intelligence, and real-world data insights. Architect, automate, and optimize AI pipelines on AWS ( SageMaker , Bedrock, Lambda, Step Functions, Redshift, Glue, S3, etc.). Integrate Generative AI and LLMs into commercial workflows, enabling NLP-based insights, sales intelligence, and … experience in AI/ML, cloud engineering, data science, or MLOps within healthcare, biotech, or life sciences. Expert knowledge of AWS cloud services, including SageMaker , Bedrock, Lambda, Step Functions, Redshift, Glue, DynamoDB . Deep expertise in AI/ML frameworks such as PyTorch , TensorFlow, Scikit-Learn, Hugging Face Transformers. More ❯

days a week We seek a skilled and proactive Senior Platform Engineer to design, build, and support scalable cloud infrastructure and automation pipelines on Amazon Web Services (AWS). You will work closely with DevOps, Security, Data, and AI Application teams to ensure platform reliability, security, and performance. Your … development, staging, and production environments. Automate operational tasks, environment provisioning, and deployments using scripting languages such as Python, Bash, or PowerShell. Enable and maintain Amazon SageMaker environments for scalable ML model training, hosting, and pipelines. Integrate AWS Bedrock to provide foundation model access for generative AI applications, ensuring … Collaborate with security and compliance teams to implement best practices around IAM, encryption, logging, monitoring, and cost optimization. Implement and manage observability tools like Amazon CloudWatch, Prometheus/Grafana, or ELK for monitoring and alerting. Support container orchestration environments using EKS (Kubernetes), ECS, or Fargate. Contribute to incident response More ❯