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.