Senior AI Developer

2 weeks ago


aligarh, India beBeeMachineLearning Full time

Machine Learning EngineerWe are seeking an experienced Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning and experience building and deploying production-ready ML systems.The responsibilities of the role include designing and implementing end-to-end ML pipelines for recommendation systems, search ranking, and classification problems, as well as developing time series forecasting models and ranking algorithms for complex business applications.Machines Learning Development & Implementation (40%):Design and implement end-to-end ML pipelines for recommendation systems, search ranking, and classification problems.Build and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks.Develop time series forecasting models and ranking algorithms for complex business applications.Implement feature engineering pipelines that handle real-world data noise and edge cases.Create robust data preprocessing and validation systems that ensure model reliability in production.Production ML Systems & Deployment (25%):Deploy ML models using Docker containerization and REST API frameworks.Implement model serving solutions on Azure Container Instances with proper monitoring and alerting.Build MLOps pipelines using MLflow for experiment tracking and model registry management.Design scalable data workflows using Apache Airflow and Azure Data Factory for ETL operations.Establish model versioning, rollback strategies, and performance monitoring in production environments.Technical Leadership & Collaboration (20%):Serve as a technical sounding board for AI team members on ML architecture and approach decisions.Mentor team members on best practices for production ML system design and implementation.Communicate complex technical concepts clearly to both technical and non-technical stakeholders.Collaborate across AI, web development, and system architecture teams to ensure seamless integration.Guide strategic decisions on when to use traditional ML versus generative AI approaches.Strategic ML Decision Making (15%):Evaluate problems to determine optimal solutions: classical ML, GenAI, or simpler analytical methods.Integrate generative AI tools effectively into workflows without over-relying on them.Design ML systems that integrate seamlessly with existing web application architectures.Provide technical guidance on model selection, evaluation metrics, and performance optimization.Stay current with ML best practices while maintaining focus on practical, business-driven solutions.


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