51 to 75 of 92 Amazon SageMaker Jobs

systems, management products, or business applications. Full-stack development experience with AI technologies/tools and apply it to user experiences or backend solutions. Experience with AI technologies like SageMaker, Vert.x, LangChain, Large Language Models, Prompt Engineering, DialogFlow, Python Experience with at least one of the following: Front-end technologies like React, Angular, SwiftUI (iOS), Kotlin (Android) Back-end More ❯

An understanding of AI, and AI ethics An understanding of data safety in use of Large Language Models Knowledge and experience of either AWS or Azure: AWS (boto3, Bedrock, Sagemaker, Lambda, S3, EC2) Azure (azure Open AI service, Cosmos DB) Python Langgraph Neo4j/cypher Other coding languages/frameworks e.g. Java/.Net AI RAG (retrieval augmented generation More ❯

at Lyst. We work mainly in Python using all the standard ML toolkits and frameworks (e.g. SKLearn, Tensorflow, Pytorch), and run our ML code in the AWS environment using Sagemaker where possible. We have a strong preference for clean, documented, well tested and reviewed code and have tooling and a culture to support this. This is a hands-on More ❯

React Native (iOS and Android) Typescript GraphQL (Apollo Client) Fastlane SwiftUI (Apple Watch) Maestro E2E tests Backend: Serverless (AWS) Lambdas (NodeJS & Python) AWS AppSync DynamoDB, S3, SQS, SNS, EventBridge, SageMaker Snowflake All the other good stuff: Sentry GitHub Actions Intercom, Mixpanel RevenueCat App Store Connect/Play Store Google Tag Manager Salary and Benefits: We offer a salary of More ❯

Lambda, Glue, API Gateway, Athena, CloudTrail, Aurora/RDS, SQS - Experience with Glue Crawlers, EUP, and Secrets Manager - Familiarity with Spark and Python for data engineering (desirable) - Experience with SageMaker Unified Studio (a plus More ❯

for data science and model development Excellent attention to detail and data quality Clear communication skills and ability to explain complex concepts Familiarity with cloud technologies like AWS and Sagemaker is advantageous Ability to work independently and collaboratively within a team TO BE CONSIDERED. Please either apply by clicking online or emailing me directly to . For further information More ❯

teams to translate data into action What You’ll Bring Strong hands-on data science and ML experience Expert in Python and modern data science libraries Experience with AWS SageMaker, Databricks, or similar cloud tools Strong background in supervised/unsupervised learning, statistical modelling, and model deployment Solid understanding of NLP techniques for document processing Domain experience in insurance More ❯

teams to translate data into action What You’ll Bring Strong hands-on data science and ML experience Expert in Python and modern data science libraries Experience with AWS SageMaker, Databricks, or similar cloud tools Strong background in supervised/unsupervised learning, statistical modelling, and model deployment Solid understanding of NLP techniques for document processing Domain experience in insurance More ❯

record of delivering high-impact machine learning models and developing algorithms that solve real-world challenges Experience programming in Python, SQL and using ML platforms and frameworks such as Sagemaker, MLflow, Seldon Core or similar Prior experience or interest in working with geospatial data Technologies we use ️ Programming languages: SQL, Python, LookML, (+ Go for other backend services) Development More ❯

technologies on client deliveries and through research Job Qualifications We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation More ❯

technologies on client deliveries and through research Job Qualifications We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation More ❯

Qualifications: Generating Echo handlers and models from OAS using oapi-codegen Launch Darkly Feature Flagging Docker AWS Cloud Services including EKS and RDS AWS Bedrock Knowledgebases and Agents AWS Sagemaker Generative AI Prompt Engineering Additional Information About QAD: QAD Inc. is a leading provider of adaptive, cloud-based enterprise software and services for global manufacturing companies. Global manufacturers face More ❯

using emerging technologies on client deliveries and through research. We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB, ... Implementation of AI/ML architectural patterns and best practices e.g. data drift detection More ❯

React Native (iOS and Android) Typescript GraphQL (Apollo Client) Fastlane SwiftUI (Apple Watch) Maestro E2E tests Backend: Serverless (AWS) Lambdas (NodeJS & Python) AWS AppSync DynamoDB, S3, SQS, SNS, EventBridge, SageMaker Postman API tests All the other good stuff: Sentry GitHub Actions Intercom, Mixpanel RevenueCat App Store Connect/Play Store Figma Software Engineer Interview Process Our aim is to More ❯

find a small reflection of our current tech stack: Frontend: React Native (iOS and Android) Typescript Fastlane SwiftUI (Apple Watch) Serverless (AWS) AWS AppSync DynamoDB, S3, SQS, SNS, EventBridge, SageMaker Postman API tests All the other good stuff: Sentry GitHub Actions Intercom, Mixpanel RevenueCat App Store Connect/Play Store Figma Software Engineer Interview Process Our aim is to More ❯

find a small reflection of our current tech stack: Frontend: React Native (iOS and Android) Typescript Fastlane SwiftUI (Apple Watch) Serverless (AWS) AWS AppSync DynamoDB, S3, SQS, SNS, EventBridge, SageMaker Postman API tests All the other good stuff: Sentry GitHub Actions Intercom, Mixpanel RevenueCat App Store Connect/Play Store Figma Software Engineer Interview Process Our aim is to More ❯

emerging technologies based on client delivery experience and research We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB, AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG More ❯

technologies based on client delivery experience and research Qualification We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB, AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG More ❯

and academia and how to apply them to solve real world customer problems We are looking for significant experience in: AI/ML platform technologies and services such as Sagemaker, Vertex, Azure ML, OpenAI, LangChain, AutoML, OCR, STT, feature stores, and vector databases. Driving literature reviews then build proof-of-concepts to validate hypotheses and thus inform AI/ More ❯

data at rest, data tagging, processing, and associated costing for each Security+ certification required Preferred Experience with IC, especially DIA, cloud implementations A working understanding of NGA Safehouse AWS SageMaker/MLSpace MarkLogic expertise More ❯

needs in operations, payments, and product workflows. Establish and maintain ML infrastructure, including CI/CD workflows for ML, model versioning, monitoring, and automated deployment. Leverage AWS services-including SageMaker, Bedrock, Lambda, Comprehend, and Rekognition-to develop secure, scalable, and cost-effective ML solutions. Set and implement best practices for the entire ML lifecycle, using tools like MLflow, SageMaker … field. 5+ years of experience in a Machine Learning Engineering or Applied ML role, with demonstrated impact in production environments. Deep hands-on experience with AWS ML services, especially SageMaker and Bedrock. Strong programming skills in Python (preferred), with additional experience in Java or Scala. Expertise in traditional ML algorithms (e.g., XGBoost, Random Forests) as well as experience working More ❯

role could be extended to a longer DevOps contract. What You'll Do - Design and build an end-to-end MLOps pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features … across training and inference environments (e.g., using SageMaker Feature Store , Feast, or custom implementation). - Work closely with data scientists to formalize feature engineering workflows , ensuring traceability, scalability, and maintainability of features. - Develop unit, integration, and data validation tests for models and features to ensure stability and quality. - Establish model monitoring and alerting frameworks for real-time and batch … data teams to adopt new MLOps practices. What We're Looking For - 3+ years of experience in MLOps, DevOps, or ML infrastructure roles. - Deep familiarity with AWS services , especially SageMaker , S3, Lambda, CloudWatch, IAM, and optionally Glue or Athena. - Strong experience with MLflow , experiment tracking , and model versioning. - Proven experience setting up and managing a feature store , and driving More ❯

scientists with DevOps issues, Docker containers, and MLOps tooling Model Deployment - Deploy Hugging Face Transformers and ML models as secure microservices AWS ML Platform - Build and evaluate models using SageMaker, Bedrock, Glue, Athena, and Redshift Knowledge Transfer - Create documentation and mentor teams on MLOps best practices Full ML Lifecycle - Manage training, validation, versioning, deployment, monitoring, and governance API Development … production deployment Data science fundamentals - Data cleaning, feature engineering, model evaluation Critical Technical Skills: Production ML deployment - Demonstrated experience maintaining AI/ML models in production AWS ML services - SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, Redshift Hugging Face deployment - NLP, vision, and generative models in AWS environments API development - Flask, FastAPI microservices and REST API frameworks DevOps integration … CI/CD pipelines, Jenkins, Maven, Chef, Git version control Cloud architecture - Working across cloud-based infrastructures Tech Stack & Tools AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch Development: Python, R, Flask, FastAPI, SQL MLOps: Apigee, Hugging Face, Jenkins, Git, Docker Environments: Jupyter, RStudio, Linux What We're Looking For Experience Level: Sr. Associate More ❯

Delivery Practice Manager - Data Analytics, ASEAN Professional Services, ASEAN Professional Services Job ID: PT Amazon Web Services Indonesia - E41 The Amazon Web Services Professional Services (ProServe) team is seeking an experienced Delivery Practice Manager (DPM) to join our ProServe Shared Delivery Team (SDT) at Amazon Web Services (AWS). In this role, you'll manage a team … to address customer outcomes. Possessing the ability to translate technical concepts into business value for customers and then talk in technical depth with teams, you will cultivate strong customer, Amazon Global Sales (AGS), and ProServe team relationships which enables exceptional business performance. DPMs success is primarily measured by consistently delivering customer engagements by supporting sales through scoping technical requirements … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯

maintain the data platform Building infrastructure and data architectures in Cloud Formation, and SAM. Designing and implementing data processing environments and integrations using AWS PaaS such as Glue, EMR, Sagemaker, Redshift, Aurora and Snowflake Building data processing and analytics pipelines as code, using python, SQL, PySpark, spark, CloudFormation, lambda, step functions, Apache Airflow Monitoring and reporting on the data … 3+ years of experience in a Data Engineering role. Strong experience and knowledge of data architectures implemented in AWS using native AWS services such as S3, DataZone, Glue, EMR, Sagemaker, Aurora and Redshift. Experience administrating databases and data platforms Good coding discipline in terms of style, structure, versioning, documentation and unit tests Strong proficiency in Cloud Formation, Python and More ❯