Machine Learning Engineer | Remote - Contract

Role Title: Machine Learning Engineer Role Type: Contract Location: Remote We are engaging Machine Learning Engineers to contribute expertise to a dynamic customer project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. Required Skills - Python - Machine Learning - MongoDB Scope of Work - Design, develop, and refine machine learning models using Python and relevant libraries to address project objectives. - Analyze large datasets and leverage MongoDB to manage and retrieve data efficiently for model training and validation. - Collaborate with cross-functional contributors to identify areas for model improvement and implement robust solutions. - Conduct thorough model evaluation, tuning hyperparameters, and benchmarking results to ensure optimal performance. - Document methodologies, experiments, and outcomes to ensure transparent and repeatable workflows. - Integrate data pipelines and preprocessing workflows to streamline training and inference processes. - Deliver actionable insights and recommendations based on data-driven findings and machine learning outcomes. Preferred Qualifications - Demonstrated expertise with Python, including deep familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch. - Hands-on experience with MongoDB for data manipulation, storage, and retrieval within machine learning projects. - Strong problem-solving skills and a track record of delivering innovative ML solutions in real-world settings. - Understanding of model evaluation metrics, feature engineering, and effective data preprocessing techniques. - Background in deploying or operationalizing machine learning models in cloud or enterprise environments. - Clear written documentation and communication skills for sharing technical findings and best practices. - Ability to adapt quickly to evolving project requirements and contribute collaboratively in a remote setting. Originally posted on Himalayas