Note: The job is a remote job and is open to candidates in USA. 24-MAG LLC makes this remote consulting opportunity available through its platform. The Machine Learning Engineer will develop and refine models, evaluate their performance, work with large datasets and MongoDB, and build data pipelines while documenting experiments and findings.
Responsibilities
- Design, develop, and refine machine learning models using Python and relevant frameworks
- Select appropriate modelling approaches based on project objectives and available data
- Implement robust training and validation workflows
- Apply feature engineering and data-preparation techniques where appropriate
- Improve model quality through iterative experimentation and analysis
- Conduct systematic model evaluation using appropriate performance metrics
- Tune hyperparameters to improve model effectiveness and stability
- Benchmark alternative models, configurations, and approaches
- Identify weaknesses, failure patterns, and opportunities for improvement
- Ensure conclusions are supported by reproducible experimental results
- Analyse and work with large datasets used for model training and validation
- Use MongoDB for efficient data storage, retrieval, and manipulation
- Develop preprocessing workflows that support reliable model development
- Integrate data pipelines across training and inference processes
- Identify and resolve data-quality or pipeline issues affecting model performance
- Document methodologies, experiments, assumptions, and outcomes clearly
- Maintain transparent and repeatable machine learning workflows
- Collaborate with cross-functional contributors to identify model-improvement opportunities
- Communicate technical findings and recommendations in a clear, actionable manner
- Adapt modelling approaches as project requirements evolve
Skills
- Demonstrated professional expertise in Python
- Strong experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or comparable tools
- Hands-on experience using MongoDB for data storage, retrieval, and manipulation
- Strong understanding of machine learning fundamentals and model-development workflows
- Familiarity with model evaluation metrics, hyperparameter tuning, and benchmarking
- Practical experience with feature engineering and data preprocessing
- Strong problem-solving ability and experience delivering real-world ML solutions
- Experience developing or integrating data pipelines for machine learning workflows
- Strong written documentation and technical communication skills
- Background deploying or operationalising machine learning models in cloud or enterprise environments is advantageous
- Ability to work effectively within a remote and evolving project environment
- Experience deploying or operationalising ML systems is highly valued
Benefits
- Fully remote
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