Databricks - SA/M - GO/JC/21461/2025

Job Description

Job Description Databricks Candidate Expectations: Possess strong experience in Azure Databricks Azure Data Factory and Azure ML Studio. Demonstrate expertise in ML Ops practices and Docker containerization. Have a solid understanding of AI & ML concepts Machine Learning and Artificial Intelligence. Show proficiency in using Terraform for infrastructure management. Be skilled in version control using GIT. Job Description: Develop and maintain data pipelines using Azure Data Factory and Azure Databricks to ensure seamless data flow. Implement machine learning models and algorithms using Azure ML Studio and Azure Machine Learning to drive business insights. Utilize ML Ops practices to streamline the deployment and monitoring of machine learning models. Leverage Docker for containerization to ensure consistent and scalable deployment of applications. Apply AI and ML concepts to solve complex business problems and improve decision-making processes. Collaborate with cross-functional teams to integrate machine learning solutions into existing systems. Use Terraform for infrastructure as code to manage and provision cloud resources efficiently. Maintain version control and collaborate on code development using GIT. Conduct thorough testing and validation of machine learning models to ensure accuracy and reliability. Provide technical support and troubleshooting for deployed machine learning solutions. Continuously monitor and optimize machine learning models to enhance performance and accuracy. Document processes models and code to ensure knowledge sharing and maintainability. Stay updated with the latest advancements in AI and ML technologies to bring innovative solutions to the team. Skills Required RoleDatabricks - SA/M Industry TypeIT/ Computers - Software Functional AreaIT-Software Required EducationAny Graduates Employment TypeFull Time, Permanent Key Skills AZURE DATABRICKS AZURE DATA FACTORY Other Information Job CodeGO/JC/21461/2025 Recruiter NameKamlesh Kumar Show more Show less