Machine Learning Engineer (Python or R)

Job Summary We are looking for a Machine Learning Engineer with 3+ years of experience in building, training, and deploying machine learning models. The ideal candidate will have strong programming skills in Python or R and hands-on experience working with data, model development, evaluation, and production deployment. You will collaborate with data scientists, software engineers, and business stakeholders to design scalable ML solutions that solve real-world problems. Key Responsibilities ● Develop, train, and deploy machine learning models using Python or R. ● Work with structured and unstructured data to build predictive and analytical solutions. ● Perform data preprocessing, feature engineering, model selection, and hyperparameter tuning. ● Evaluate model performance using appropriate metrics and validation techniques. ● Build and maintain ML pipelines for training, testing, and inference. ● Collaborate with data engineers and software teams to integrate models into applications and workflows. ● Develop APIs or services to expose machine learning models for production use. ● Monitor model performance in production and retrain models as needed. ● Analyze business requirements and translate them into machine learning solutions. ● Document model design, experiments, and deployment processes. ● Troubleshoot, optimize, and maintain ML systems in production environments. Required Qualifications ● Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. ● 3+ years of experience in Machine Learning, Data Science, or Software Development. ● Strong programming skills in Python or R. ● Hands-on experience with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch, caret, tidymodels, or similar tools. ● Solid understanding of supervised and unsupervised learning techniques. ● Experience with data preprocessing, feature engineering, and model evaluation. ● Knowledge of SQL and working with relational or non-relational databases. ● Experience building and deploying ML models in production environments. ● Familiarity with Git, Docker, and CI/CD workflows. ● Strong analytical, problem-solving, and communication skills. Preferred Qualifications ● Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. ● Familiarity with MLOps tools such as MLflow, Kubeflow, Airflow, or SageMaker. ● Experience with model monitoring, drift detection, and retraining strategies. ● Knowledge of NLP, computer vision, time series forecasting, or recommendation systems. ● Experience working in Agile or cross-functional product teams. ● Exposure to big data tools such as Spark or Databricks.