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Description:
Spinify is seeking a Senior AWS Machine Learning Engineer to help build and maintain an innovative platform that integrates coaching, learning, and AI through personalized experiences, predictive analytics, and real-time feedback.
The role involves developing scalable Machine Learning models using AWS SageMaker for personalized learning and performance coaching.
Responsibilities include managing real-time data pipelines with AWS Kinesis and Glue, implementing natural language querying with AWS Q, and utilizing Amazon Personalize for personalized learning paths.
The engineer will also work on emotional intelligence analysis using Amazon Rekognition and Comprehend, create real-time dashboards with Amazon QuickSight, and build serverless workflows with AWS Lambda and API Gateway.
Integration of generative AI models using AWS Bedrock, ensuring data privacy and security with AWS IAM, and compliance with GDPR and SOC2 are key aspects of the role.
Requirements:
5+ years of experience with AWS Cloud Services, specifically SageMaker, Kinesis, Glue, Lambda, Personalize, and QuickSight.
Proficiency in machine learning, including model training, optimization, and deployment using AWS SageMaker.
Experience in building and managing real-time data pipelines with Kinesis, integrating large datasets with Glue, and expertise in Natural Language Processing (NLP) using Amazon Comprehend.
Familiarity with AWS Bedrock, Python, SQL, API development with API Gateway and Lambda, real-time data visualizations with QuickSight, and data security best practices with AWS IAM.
Strong analytical and problem-solving skills, with the ability to optimize performance and scale solutions.
Benefits:
Competitive salary with performance-based bonuses.
Remote work flexibility.
Opportunity to work with cutting-edge AI and cloud technologies.
A collaborative and innovative team environment.
Opportunities for professional development and growth in the AI and ML space.