Machine Learning Solutions Engineer, AssetWatch

• 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

Desirable

• We're seeking an experienced Machine Learning Solutions Engineer to join our team and play a critical role in developing, deploying, and managing machine learning models on AWS
• You will collaborate with cross-functional teams to implement end-to-end ML pipelines, ensure seamless model deployment, and continuously monitor and evaluate model performance
• As a subject matter expert in MLOps, you will also mentor other engineers and contribute to the development of our company's ML engineering capabilities
• Infrastructure & Pipeline Management: Set up optimal ML infrastructure on AWS and construct a robust pipeline, covering data preprocessing, model training, and tuning
• Data Ingestion & Feature Management: Ensure efficient data ingestion mechanisms and design Feature Store Data Models for streamlined storage of engineered features
• AI Model Deployment on AWS: Seamlessly transition trained models into AWS production environments, ensuring integration and performance
• Automation & Scaling: Streamline the model training process with automation, ensuring scalability and adaptability
• Inference Pipelines: Craft inference mechanisms post-training that prioritize client load balancing and utilize containerization
• Security & Version Control: Implement top-tier security standards, especially during deployment, and maintain best practices for ML model and data versioning
• Model Monitoring & Evaluation: Establish mechanisms for data drift or concept drift post-deployment and initiate A/B tests to guide model refinements
• Team Engagement & Continuous Learning: Collaborate with interdisciplinary teams throughout the model's lifecycle and stay updated on MLOps trends, AWS, and machine learning innovations