Machine Learning Solutions Engineer

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Requirements:

• BS or MS in Computer Science, Computer Engineering, or related field.
• At least 5 years of industrial experience in ML and with AWS.
• Demonstrable experience in deploying and prototyping AI models on AWS.
• Hands-on experience with specific AWS compute tools including AWS/Amazon SageMaker, container and deployment in ECS, Lambda, etc.
• Proficiency with various purpose-built databases within AWS, particularly those optimized for ML workloads.
• Strong proficiency in programming languages such as Python and SQL.
• Deep understanding of containerization techniques, especially in the context of ML model inference.

Responsibilities:

• Set up optimal ML infrastructure on AWS and construct a robust pipeline, covering data preprocessing, model training, and tuning.
• Ensure efficient data ingestion mechanisms and design Feature Store Data Models for streamlined storage of engineered features.
• Seamlessly transition trained models into AWS production environments, ensuring integration and performance.
• Streamline the model training process with automation, ensuring scalability and adaptability.
• Craft inference mechanisms post-training that prioritize client load balancing and utilize containerization.
• Implement top-tier security standards, especially during deployment, and maintain best practices for ML model and data versioning.
• Establish mechanisms for data drift or concept drift post-deployment and initiate A/B tests to guide model refinements.
• Collaborate with interdisciplinary teams throughout the model's lifecycle and stay updated on MLOps trends, AWS, and machine learning innovations.