• Develop end-to-end ML solutions including data preprocessing, model development, training, evaluation, and deployment.

• Implement machine learning algorithms for classification, regression, clustering, recommendation systems, anomaly detection, or forecasting.

• Collaborate with data engineering teams to build scalable ML pipelines and integrate models into production systems.

• Conduct exploratory data analysis (EDA) and feature engineering to improve model performance.

• Monitor and optimize models post-deployment for accuracy, latency, and efficiency.

• Work with cross-functional stakeholders to translate business problems into data-driven solutions.

• Utilize cloud platforms (AWS, GCP, Azure) for ML experimentation, training, and deployment.

• Document methodologies, experiments, and model performance using reproducible practices.

• Stay updated with advancements in machine learning, deep learning, and MLOps technologies.

Required Skills & Experience

• Strong programming skills in Python and ML libraries (e.g., scikit-learn, Pandas, NumPy).

• Experience with Deep Learning frameworks such as TensorFlow or PyTorch (as applicable).

• Solid understanding of machine learning algorithms, statistical modeling, and model evaluation metrics.

• Experience with SQL/NoSQL databases and handling large structured/unstructured datasets.

• Familiarity with MLOps workflows (CI/CD, model deployment, monitoring, retraining).

• Knowledge of data visualization tools (Matplotlib, Seaborn, Plotly).

• Experience with cloud services (AWS, Azure, GCP) for ML workloads.

• Strong problem-solving, analytical and communication skills.