Note: The job is a remote job and is open to candidates in USA. Celebal Technologies is seeking a Machine Learning Engineer to design, develop, deploy, and optimize scalable machine learning solutions using Databricks. The role focuses on ML pipelines, model development, MLOps, large-scale data platforms, and production deployments.
Responsibilities
- Design, develop, and deploy machine learning models for predictive analytics, classification, recommendation systems, and other business use cases
- Build and maintain scalable ML pipelines using Databricks, PySpark, and MLflow
- Develop and optimize data ingestion, transformation, and feature engineering workflows
- Implement MLOps best practices, including model versioning, deployment, monitoring, and retraining
- Work with large-scale structured and unstructured datasets within the Databricks Lakehouse environment
- Collaborate with Data Engineers, Data Scientists, and business stakeholders to deliver production-ready ML solutions
- Optimize model performance, scalability, and operational efficiency
- Ensure data quality, governance, security, and compliance standards are followed
- Troubleshoot and resolve issues related to data pipelines, model performance, and production deployments
Skills
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field
- 5+ years of experience in Machine Learning, Data Science, or AI-related roles
- Strong hands-on experience with Databricks (mandatory)
- Expertise in Databricks ML, Delta Lake, Unity Catalog, MLflow, and Databricks Workflows
- Strong proficiency in Python, PySpark, SQL, and Spark
- Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM
- Experience building and deploying machine learning models in production environments
- Strong understanding of feature engineering, model evaluation, hyperparameter tuning, and model monitoring
- Experience with cloud platforms such as Azure, AWS, or GCP
- Knowledge of CI/CD pipelines and MLOps practices
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