Description

Key Responsibilities
• Model Development: Design, build, and train machine learning and deep learning models for real-world applications
• Data Pipeline Construction: Develop and maintain scalable data pipelines for data collection, cleaning, transformation, and ingestion
• Evaluation & Optimization: Run experiments, analyze performance metrics (e.g., accuracy, recall, AUC), and optimize models for performance, speed, and scalability
Required Skills & Qualifications
• Programming: Strong proficiency in Python (including PySpark) with solid understanding of software architecture principles
• ML Frameworks: Hands-on experience with machine learning libraries such as Scikit-learn
• Mathematics: Strong foundation in statistics, probability, and algorithm design
• Data Handling: Experience with SQL, data modeling, and building data pipelines
Nice to Have
• Production Deployment (MLOps): Experience deploying models using tools like Docker
• LLM Experience: Familiarity with deploying and working with local large language models (LLMs)

 

Key Skills

Education

Bachelor's degree