Job Description
We are looking for a highly motivated experienced Machine Learning Engineer to join our team. You will design, develop, and deploy machine learning models across diverse business use cases. This role provides hands‑on exposure to classical ML, deep learning, and emerging Generative AI technologies. The ideal candidate has strong Python skills, solid understanding of ML fundamentals, and a passion for solving real‑world problems through data‑driven solutions.
Job Description
Core Responsibilities
- Build, train, evaluate, andoptimizeML 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.
- Engineermeaningful, high‑impact features from raw data using domain understanding, transformations, and derived metrics to significantly improve model performance.
- Identifyand select the most relevant and meaningful features using statistical and modelbased 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 likePyTorchor 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.
