Module Lead - Machine Learning Engineer

Job Description

Role & responsibilities We are seeking an experienced and technically strong Machine Learning Engineer to design, implement, and operationalize ML models across Google Cloud Platform (GCP) and Microsoft Azure. The ideal candidate will have a robust foundation in machine learning algorithms, MLOps practices, and experience deploying models into scalable cloud environments. Responsibilities: Design, develop, and deploy machine learning solutions for use cases in prediction, classification, recommendation, NLP, and time series forecasting. Translate data science prototypes into production-grade, scalable models and pipelines. Implement and manage end-to-end ML pipelines using: Azure ML (Designer, SDK, Pipelines), Data Factory, and Azure Databricks Vertex AI (Pipelines, Workbench), BigQuery ML, and Dataflow Build and maintain robust MLOps workflows for versioning, retraining, monitoring, and CI/CD using tools like MLflow, Azure DevOps, and GCP Cloud Build. Optimize model performance and inference using techniques like hyperparameter tuning, feature selection, model ensembling, and model distillation. Use and maintain model registries, feature stores, and ensure reproducibility and governance. Collaborate with cloud architects, and software engineers to deliver ML-based solutions. Maintain and monitor model performance in production using Azure Monitor, Prometheus, Vertex AI Model Monitoring, etc. Document ML workflows, APIs, and system design for reusability and scalability. Primary Skills required (Must Have Expereince): 5 -7 years of experience in machine learning engineering or applied ML roles. Advanced proficiency in Python, with strong knowledge of libraries such as Scikit-learn, Pandas, NumPy, XGBoost, LightGBM, TensorFlow, PyTorch. Solid understanding of core ML concepts: supervised/unsupervised learning, cross-validation, bias-variance tradeoff, evaluation metrics (ROC-AUC, F1, MSE, etc.). Hands-on experience deploying ML models using: Azure ML (Endpoints, SDK), AKS, ACI Vertex AI (Endpoints, Workbench), Cloud Run, GKE Familiarity with cloud-native tools for storage, compute, and orchestration: Azure Blob Storage, ADLS Gen2, Azure Functions GCP Storage, BigQuery, Cloud Functions Experience with containerization and orchestration (Docker, Kubernetes, Helm). Strong understanding of CI/CD for ML, model testing, reproducibility, and rollback strategies. Experience implementing drift detection, model explainability (SHAP, LIME), and responsible AI practices.