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.
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