Machine Learning Engineer

Key ResponsibilitiesDesign, develop, train, and deploy machine learning models.Collect, clean, preprocess, and analyze structured and unstructured datasets.Develop predictive models and machine learning solutions for business problems.Select appropriate algorithms, features, and modeling techniques.Evaluate model performance using appropriate metrics and improve model accuracy.Perform feature engineering, model tuning, and validation.Build scalable ML pipelines for training and inference.Deploy machine learning models into production environments.Monitor model performance, data quality, and model drift.Integrate ML models with applications through APIs and services.Collaborate with Data Scientists, Data Engineers, Software Developers, and Product teams.Troubleshoot and optimize machine learning systems for performance and scalability.Document models, experiments, workflows, and deployment processes.Research and implement new machine learning techniques and tools.Required SkillsProgrammingStrong proficiency in Python.Good knowledge of object-oriented programming and software development principles.Experience with Git/GitHub or similar version-control systems.Machine LearningStrong understanding of supervised and unsupervised learning.Knowledge of regression, classification, clustering, decision trees, ensemble methods, and dimensionality reduction.Experience with feature engineering and model evaluation.Understanding of cross-validation, hyperparameter tuning, and model optimization.Knowledge of statistics and probability.ML Libraries & FrameworksExperience with Scikit-learn.Knowledge of TensorFlow or PyTorch.Familiarity with Pandas, NumPy, Matplotlib, or similar data-science libraries.Data & DatabasesGood knowledge of SQL.Experience working with structured and unstructured datasets.Understanding of data preprocessing and data pipelines.Knowledge of databases such as MySQL, PostgreSQL, MongoDB, or SQL Server is an advantage.Deployment & MLOpsUnderstanding of REST APIs and model serving.Familiarity with Docker and containerization.Knowledge of cloud platforms such as AWS, Azure, or Google Cloud.Understanding of CI/CD and MLOps practices is preferred.Experience with model monitoring and versioning is a plus.Preferred SkillsExperience with Deep Learning.Knowledge of Natural Language Processing (NLP) or Computer Vision.Familiarity with Generative AI and Large Language Models (LLMs).Experience with MLflow, Kubeflow, or similar MLOps tools.Knowledge of Kubernetes is an advantage.Experience deploying models in production environments.