Machine Learning Engineer (Python)

Position Overview: We are seeking a talented Python Machine Learning Engineer with 1 to 4 years of experience to join our dynamic team. The ideal candidate will be passionate about leveraging machine learning algorithms to develop and maintain recommender systems that personalize user experiences and drive engagement. Key Responsibilities: Design, develop, and deploy scalable recommender systems using popular machine learning algorithms such as collaborative filtering, matrix factorization, and deep learning techniques. Collaborate closely with cross-functional teams to understand business requirements and translate them into actionable machine learning solutions. Conduct thorough exploratory data analysis to identify relevant features and patterns in large-scale datasets. Implement and optimize machine learning models for performance, scalability, and efficiency. Continuously monitor and evaluate model performance using relevant metrics and implement necessary improvements. Document code, methodologies, and experiment results for reproducibility and knowledge sharing purposes. Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field. 2 to 3 years of hands-on experience in developing machine learning models, with a focus on recommender systems. Proficiency in Python programming and popular machine learning libraries/frameworks such as TensorFlow, PyTorch, or Scikit-learn. Solid understanding of fundamental machine learning concepts including supervised and unsupervised learning, feature engineering, and model evaluation. Experience working with large-scale datasets Strong analytical and problem-solving skills with a keen attention to detail. Excellent communication and collaboration abilities, with the capacity to explain complex technical concepts to non-technical stakeholders. Ability to thrive in a fast-paced, dynamic environment and adapt to changing priorities. Working with SQL and NoSQL databases to store and retrieve training data. Writing efficient ETL pipelines to feed real-time and batch ML models using Apache Airflow. Preferred Qualifications: Experience with cloud computing platforms such as AWS (Knowledge of Redshift, Athena, RDS, Spectrum). Familiarity with recommendation system evaluation techniques such as precision, recall, and AUC-ROC. Knowledge of natural language processing (NLP) techniques and text-based recommendation systems. Contributions to open-source machine learning projects or participation in relevant competitions (e.g., Kaggle) is a plus. MLOps & Deployment (Docker, Airflow). Cloud Platforms (AWS, GCP, Azure, SageMaker).