Senior Machine Learning Engineer
2 weeks ago
Key Responsibilities:Develop and implement end-to-end machine learning pipelines for recommendation systems, search ranking, and classification tasks.Buuild and optimize traditional machine learning models using ensemble methods, support vector machines, gradient boosting, and neural networks.Design and 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.Establish robust data preprocessing and validation systems to ensure model reliability in production environments.Production ML Systems & Deployment:Deploy machine learning models using containerization and REST API frameworks.Implement model serving solutions on cloud-based platforms with proper monitoring and alerting.Build MLOps pipelines using experiment tracking and model registry management tools.Design scalable data workflows using data pipeline development and ETL processes.Technical Leadership & Collaboration:Provide technical guidance and mentorship to team members on machine learning architecture and approach decisions.Communicate complex technical concepts clearly to both technical and non-technical stakeholders.Collaborate across teams to ensure seamless integration of machine learning systems.Guide strategic decisions on machine learning approaches and technologies.Strategic ML Decision Making:Evaluate problems to determine optimal machine learning solutions.Integrate generative AI tools into workflows effectively.Design machine learning systems that integrate seamlessly with existing web application architectures.Provide technical guidance on model selection, evaluation metrics, and performance optimization.Required Skills and Qualifications:Master's or Bachelor's degree in Computer Science, Data Science, Statistics, or related fields.4+ years of hands-on experience building and deploying machine learning systems in production environments.Proven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.).Track record of mentoring technical team members and leading collaborative projects.Core Technical Skills:Expert-level Python proficiency with a focus on clean, maintainable, production-ready code.Deep understanding of classification, regression, ranking, and recommendation algorithms.Experience with MLOps practices, model deployment, monitoring, and lifecycle management.Proficiency with data pipeline development, ETL processes, and handling messy real-world datasets.Hands-on experience with cloud-based platforms and data storage solutions.Preferred Qualifications:Knowledge of natural language processing techniques and text classification systems.Background in building machine learning systems for talent acquisition, recruiting, or HR technology.Experience with real-time machine learning inference and low-latency model serving.Understanding of distributed computing and large-scale data processing.
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