Machine Learning Engineer

תיאור משרה

We are seeking a highly skilled Machine Learning Engineer with expertise in Automation Tools and Natural Language Processing (NLP) to join our team. In this role, you will work on designing, building, and deploying ML models and automation pipelines that enhance the efficiency and intelligence of our systems. You will leverage cutting-edge technologies such as Amazon Bedrock, SageMaker Studio, and other NLP tools to develop robust, scalable solutions for business challenges and enhance business processes across various departments, including Customer Operations, Revenue Growth, and Online Marketing. The ideal candidate will have hands-on experience working with both structured and unstructured data, leveraging automation tools to streamline workflows and improve business outcomes.
Automation Tools Expertise: Create and manage automated workflows and pipelines. Streamline data ingestion, processing, model training, and deployment pipelines to increase efficiency and reduce manual effort.
Data Integration and Pipeline Automation: Automate the handling of structured data (e.g., from databases and APIs) and unstructured data (e.g., from customer reviews, emails, and social media) to drive intelligent insights for decision-making. Use Amazon Bedrock and SageMaker Studio for seamless integration
Collaborate with Data Engineers: Work with data engineers to ensure seamless integration of machine learning models into data pipelines, ensuring data is prepared and processed correctly for training and inference.
Deploy and Monitor Models: Implement best practices for deploying and maintaining machine learning models in production, ensuring scalability, performance, and reliability. Utilize AWS SageMaker and Bedrock for model management and deployment.
Optimization and Troubleshooting: Continuously improve models by troubleshooting issues and optimizing performance through fine-tuning, hyperparameter tuning, and advanced techniques like transfer learning and model compression.
Optimize Marketing Campaigns: Use machine learning models to automate and optimize marketing campaigns by analyzing customer data, improving customer targeting, segmentation, and personalizing marketing messages.
Data and Model Monitoring: Set up automated monitoring tools to track the performance of data pipelines and models, providing real-time insights and alerts for issues such as data drift or model degradation.
Model Monitoring & Optimization: Continuously monitor model performance, troubleshoot issues, and implement improvements to ensure that deployed models meet business objectives, and are scalable and efficient.
Data Analysis and Insights: Analyze large datasets to uncover patterns, trends, and actionable insights that drive strategic decisions for customer operations and revenue growth.Collaborate Across Departments: Partner with Customer Operations, Revenue Growth, Online Marketing, and other departments to understand their challenges and develop tailored machine learning solutions to meet business needs.
Stay Up-to-date with Industry Trends: Keep up with the latest developments in machine learning, AI, and NLP. Integrate new techniques, tools, and frameworks as appropriate.
Document and Report Findings: Maintain clear documentation of models, processes, and code. Provide regular reports to stakeholders on model performance, automation efficiency, and overall system improvements.