Machine Learning Expert

2 weeks ago


alappuzha, India beBeeExpert Full time

Machine Learning ExpertWe are seeking a skilled Machine Learning Expert to join our team. In this role, you will be responsible for designing and implementing end-to-end machine learning pipelines for recommendation systems, search ranking, and classification problems.You will build and optimize traditional machine learning models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks. Additionally, you will develop time series forecasting models and ranking algorithms for complex business applications.In the production ML Systems & Deployment segment, you will deploy ML models using Docker containerization and REST API frameworks. You will also implement model serving solutions on Azure Container Instances with proper monitoring and alerting.As a technical leader, you will serve as a sounding board for AI team members on ML architecture and approach decisions. You will mentor team members on best practices for production ML system design and implementation.This is a key role in ensuring the seamless integration of machine learning into our business operations. The successful candidate will have a strong background in machine learning and experience working with large datasets.Required Qualifications:Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field.4+ years of hands-on experience building and deploying machine learning systems in production.Proven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.).Core Technical Skills:Programming Excellence: Expert-level Python proficiency with focus on clean, maintainable, production-ready code.Traditional ML Expertise: Deep understanding of classification, regression, ranking, and recommendation algorithms.Production ML: Experience with MLOps practices, model deployment, monitoring, and lifecycle management.Data Engineering: Proficiency with data pipeline development, ETL processes, and handling messy real-world datasets.



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