AI / ML Engineer – Revol Software

The responsibilities of an AI/ML Engineer typically include:

Model Development: Design, develop, and deploy machine learning models for various applications, including classification, regression, clustering, and recommendation systems.

Data Preprocessing: Clean, preprocess, and manipulate raw data to make it suitable for machine learning, including feature engineering, handling missing data, and normalization.

Algorithm Selection: Evaluate and select appropriate machine learning algorithms based on the problem requirements, performance metrics, and data characteristics.

Model Training: Train machine learning models using large datasets, adjusting hyperparameters to improve performance.

Model Evaluation and Tuning: Measure model performance using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC, and fine-tune models for optimal results.

Integration and Deployment: Work with software engineers to integrate machine learning models into production environments and ensure scalability.

Research and Innovation: Stay up-to-date with the latest AI/ML research and techniques, incorporating new technologies and methods to improve model performance.

Collaboration: Collaborate with cross-functional teams, including data scientists, product managers, and software developers, to solve complex problems using AI/ML.

Problem Solving: Identify business problems, translate them into machine learning tasks, and provide data-driven solutions.

Data Infrastructure Management: Build and maintain the infrastructure for handling large-scale datasets, including data pipelines and storage systems.

Monitoring and Maintenance: Continuously monitor the performance of deployed models and address issues like data drift, model decay, or underperformance.

Documentation and Reporting: Document processes, model parameters, evaluation metrics, and results for transparency and reproducibility. Communicate findings to stakeholders in a clear and understandable manner.