Senior Machine Learning Engineer (Classical ML & MLOps)

About the Role We are seeking a Senior Machine Learning Engineer with strong expertise in classical machine learning algorithms , model optimization , and end-to-end MLOps deployment . You will design, build, and operationalize scalable ML pipelines and ensure that ML models transition smoothly from experimentation to production. The ideal candidate combines deep technical skills with hands-on experience in automating and maintaining ML systems at scale. Key Responsibilities Machine Learning Development Design, train, and validate classical ML models (regression, tree-based models, ensemble methods, clustering, anomaly detection, etc.) for structured/tabular datasets. Collaborate with data scientists and data engineers to translate analytical models into production-grade code . Perform feature engineering, model tuning, and evaluation using frameworks like Scikit-learn, XGBoost, LightGBM, or CatBoost. Analyze data quality, perform exploratory data analysis (EDA), and create reusable feature stores. MLOps & Productionization Build and automate end-to-end ML pipelines for training, testing, deployment, monitoring, and retraining. Manage model lifecycle using tools like MLflow , Kubeflow , Vertex AI , SageMaker , or Azure ML Studio . Implement CI/CD pipelines for ML workflows using GitHub Actions, Jenkins, or GitLab CI . Develop containerized ML services using Docker and orchestrate them via Kubernetes or ECS/EKS . Design and monitor model performance metrics , data drift , and concept drift in production environments. Work closely with DevOps and Data Engineering teams to integrate ML services with backend systems and APIs.