Machine Learning Engineer 2

Key Responsibilities:

• Design, develop, train, evaluate, and optimize ML models for various tasks including classification, regression, and clustering.
• Apply classical ML algorithms like Logistic Regression, Decision Trees, Random Forests, SVMs, and Gradient Boosting.
• Engineer impactful features from raw data using domain knowledge and advanced techniques to enhance model performance.
• Utilize statistical and model-based methods for feature selection, and employ interpretability tools like SHAP to understand feature contributions.
• Perform comprehensive data preprocessing, including handling missing values, outlier treatment, normalization, and encoding.
• Conduct exploratory data analysis (EDA) to uncover data patterns and insights.
• Implement and fine-tune deep learning models using frameworks such as PyTorch or TensorFlow.
• Design and execute experiments to compare model performance and clearly communicate findings.
• Engage with Generative AI use cases, including embeddings, summarization, and LLM-based solutions.
• Experiment with modern AI architectures like Transformers, BERT, and GPT-style models.
• Write clean, modular, and well-documented Python code for ML solutions.
• Develop end-to-end ML pipelines for training, validation, and inference.
• Collaborate with engineering teams to deploy ML models into production environments.
• Stay abreast of the latest advancements in ML, DL, and GenAI research and apply relevant techniques.