Machine Learning Engineer (Python or R)

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., * 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.

• 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.