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.