AI/ML Trainee

Overview:   
Enterprise64 is a U.S.-based technology company that creates digital solutions for startups to Fortune 500 companies in the United States, Europe, and the United Arab Emirates. We are growing rapidly across all our global offices and have career opportunities for Trainees who are looking to work in a fast-growing company, work in a fun environment, looking to be challenged, grow, and fast-track their careers.
Job Description:
We are seeking enthusiastic AI/ML Trainee to contribute to our machine learning and data engineering projects. This role offers a unique opportunity to work on innovative AI solutions, from data preprocessing to model deployment, including working with Retrieval-Augmented Generation (RAG) systems and data ingestion processes.

Responsibilities:

• Assist in building machine learning models using Python, TensorFlow, or similar frameworks.
• Participate in data preprocessing, feature selection, and model training.
• Experiment with different algorithms to improve model accuracy.
• Work with large datasets, performing data cleaning and transformation.
• Support the implementation of data pipelines, model deployment, and data ingestion processes.
• Assist in designing, developing, and maintaining data storage solutions (e.g., databases, data lakes).
• Collaborate on data integration and ETL (Extract, Transform, Load) processes.
• Ensure data quality and integrity throughout the data lifecycle.
• Contribute to the development and deployment of RAG (Retrieval-Augmented Generation) systems.
• Stay updated with the latest AI/ML and data engineering trends and techniques.
• Contribute ideas for new AI/ML applications, RAG systems, and enhancements.

Requirements:

• Bachelor's in Computer Science/ Software Engineering are encouraged to apply
• Pursuing AI/ML-related certification/degree is a plus.
• Proficiency in Python and familiarity with ML libraries (e.g., TensorFlow, scikit-learn).
• Basic understanding of data science principles, data ingestion, RAG systems, and statistical analysis.
• Familiarity with data engineering tools and techniques (e.g., SQL, data pipelines, ETL processes).
• Strong analytical skills and a passion for AI/ML and data engineering.