Key Responsibilities

• Design, train, and optimize machine learning and deep learning models (CNN, RNN/LSTM)
• Perform feature engineering and model optimization for production use cases
• Work with scientific and signal processing datasets, including mass spectrometry data where applicable
• Build and maintain data preprocessing pipelines using Pandas and NumPy
• Deploy ML models as services using FastAPI, Flask, or Django
• Set up and manage ML experiment tracking and MLOps workflows using MLflow and Docker
• Collaborate with the team using Git for version control
• Work with PostgreSQL/MySQL (and optionally MongoDB) for data storage and retrieval

Requirements

• 3-5 years of experience in Python-based machine learning engineering
• Strong hands-on experience with PyTorch, TensorFlow, and scikit-learn
• Solid understanding of machine learning and deep learning concepts (CNN, RNN/LSTM)
• Experience with feature engineering and model optimization
• Proficiency in Pandas, NumPy, and data preprocessing techniques
• Experience with signal processing and scientific datasets; mass spectrometry experience is a strong plus
• Familiarity with FastAPI, Flask, or Django for model deployment
• Working knowledge of MLflow, Docker, Git, and MLOps fundamentals
• Experience with PostgreSQL or MySQL; MongoDB experience is a plus

Skills Required