Engineer 2 - Machine Learning

Core Responsibilities

• Build, train, evaluate, and optimize ML models for classification, regression, clustering, and other tasks.
• Apply classical ML algorithms such as Logistic Regression, Decision Trees, Random Forests, SVMs, and Gradient Boosting to solve business problems .
• Engineer meaningful, high ‑ impact features from raw data using domain understanding, transformations, and derived metrics to significantly improve model performance.
• I dentify and select the most relevant and meaningful features using statistical and model based techniques, and leverage model interpretability tools like SHAP to understand feature contributions, reduce redundancy, and improve overall model performance.
• Perform data preprocessing, including handling missing values, outlier treatment, normalization, and encoding categorical variables.
• Conduct exploratory data analysis (EDA) to understand data patterns and insights.
• Implement and fine ‑ tune deep learning models using frameworks like PyTorch or TensorFlow.
• Run experiments, compare model performance, and communicate results with clear reasoning.
• Work on GenAI use cases, such as embeddings, summarization, and LLM ‑ based solutions.
• Experiment with modern architectures (Transformers, BERT, GPT ‑ style models).
• Write clean, modular, and well ‑ documented Python code.
• Develop end ‑ to ‑ end ML pipelines for training, validation, and inference.
• Collaborate with engineers to deploy models into production environments.
• Stay updated with latest ML/DL/GenAI research and apply relevant techniques .

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