Machine Learning Enginee

RESOURCES ID is hiring a Machine Learning Engineer to design, implement, and optimize models supporting its data-driven initiatives. The role involves preparing datasets, evaluating and refining models, collaborating with stakeholders, and monitoring models in production.

Responsibilities:

• This part-time remote Machine Learning Engineer role focuses on designing, implementing, and optimizing machine learning models that support RESOURCES ID’s data-driven initiatives
• Day-to-day activities include exploring and preprocessing datasets, building and evaluating models, and refining algorithms for pattern recognition and predictive analytics
• The engineer will collaborate with cross-functional stakeholders to translate business needs into technical specifications, document workflows, and present findings in clear, actionable formats
• Responsibilities also include monitoring model performance in production, iterating on model improvements, and maintaining code quality and reproducibility using modern development practices

Requirements:

• Candidates should possess strong foundations in Computer Science and Algorithms, including data structures, complexity analysis, and software engineering principles
• Candidates should possess skills in Pattern Recognition and Neural Networks, with experience building and evaluating models for classification, regression, or anomaly detection
• Candidates should possess knowledge of Statistics, including probability, hypothesis testing, and model evaluation metrics
• Candidates should possess proficiency in programming languages commonly used in machine learning (such as Python) and frameworks (such as TensorFlow, PyTorch, or scikit-learn)
• Candidates should possess the ability to work with real-world datasets, including data cleaning, feature engineering, and handling missing or noisy data
• Candidates should possess strong problem-solving, analytical, and communication skills, with the ability to explain technical concepts to non-technical stakeholders
• Relevant experience with cloud platforms, MLOps practices, version control (e.g., Git), and deployment of models to production environments is beneficial
• A bachelor's degree or higher in Computer Science, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience, is preferred