MLOP's Engineer

Responsibilities Design, implement, and manage end-to-end ML pipelines for model training, testing, and deployment Deploy and maintain machine learning models in production environments Develop and optimize data pipelines using SQL (mandatory) Work closely with Data Scientists and Data Engineers to operationalize ML models Build and maintain CI/CD pipelines for ML workflows Monitor model performance and ensure scalability and reliability Utilize Databricks for data engineering, model development, and deployment Ensure best practices in data governance, versioning, and reproducibility Troubleshoot and resolve production issues efficiently Qualifications Strong experience in ML Ops (Model Deployment, Monitoring, CI/CD) Hands-on experience with Databricks Strong SQL expertise (mandatory) for data manipulation and pipeline development Good communication and stakeholder management skills Experience with cloud platforms (AWS/Azure/GCP) Bachelor's or Master's degree in Computer Science, Engineering, or related field 4+ years of relevant experience in ML Ops / Data Engineering / ML Engineering Skills: databricks,ml,ops