Machine Learning Engineer

About the job

Job Summary Responsible for contributing to the development and deployment of machine learning algorithms. Evaluates accuracy and functionality of machine learning algorithms as a part of a larger team. Contributes to translating application requirements into machine learning problem statements. Analyzes and evaluates solutions both internally generated as well as third party supplied. Contributes to developing ways to use machine learning to solve problems and discover new products, working on a portion of the problem and collaborating with more senior researchers as needed. Works with moderate guidance in own area of knowledge. About the Role: 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. Roles and 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. • Identify 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.