Data Science Expert

1 week ago

vijayawada, India beBeeMachineLearning Full time

Job Title: Machine Learning EngineerThis is a unique opportunity to work on cutting-edge projects and drive innovation in the field of machine learning.The successful candidate will be responsible for designing and implementing end-to-end ML pipelines for recommendation systems, search ranking, and classification problems.They will build and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks.Additionally, they will develop time series forecasting models and ranking algorithms for complex business applications.The ideal candidate will have strong expertise in feature engineering pipelines that handle real-world data noise and edge cases.They will also create robust data preprocessing and validation systems that ensure model reliability in production.Production ML Systems & DeploymentThe Production ML Systems & Deployment section involves deploying ML models using Docker containerization and REST API frameworks.The successful candidate will implement model serving solutions on Azure Container Instances with proper monitoring and alerting.They will also build MLOps pipelines using MLflow for experiment tracking and model registry management.Additionally, they will design scalable data workflows using Apache Airflow and Azure Data Factory for ETL operations.Finally, they will establish model versioning, rollback strategies, and performance monitoring in production environments.Technical Leadership & CollaborationThe Technical Leadership & Collaboration section includes serving as a technical sounding board for AI team members on ML architecture and approach decisions.The successful candidate will mentor team members on best practices for production ML system design and implementation.They will communicate complex technical concepts clearly to both technical and non-technical stakeholders.Additionally, they will collaborate across AI, web development, and system architecture teams to ensure seamless integration.Finally, they will guide strategic decisions on when to use traditional ML versus generative AI approaches.Strategic ML Decision MakingThe Strategic ML Decision Making section involves evaluating problems to determine optimal solutions: classical ML, GenAI, or simpler analytical methods.The successful candidate will integrate generative AI tools effectively into workflows without over-relying on them.They will design ML systems that integrate seamlessly with existing web application architectures.Additionally, they will provide technical guidance on model selection, evaluation metrics, and performance optimization.Finally, they will stay current with ML best practices while maintaining focus on practical, business-driven solutions.],

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