Design, develop, and deploy machine learning models using MLOps tools such as Azure DevOps, AWS SageMaker, and Databricks.
Collaborate with cross-functional teams to integrate ML models into larger software systems.
Develop data pipelines using PySpark, SQL, and IAC tools to extract insights from large datasets.
Implement continuous integration and deployment (CI/CD) pipelines using Jenkins and Terraform for seamless model updates.
Troubleshoot issues related to ML model performance and scalability.
Desired Candidate Profile
9-11 years of experience in Machine Learning Engineering or a related field.
Strong proficiency in programming languages like Python and familiarity with popular libraries like NumPy, Pandas, Matplotlib.
Experience working on cloud platforms like AWS or GCP; knowledge of Azure DevOps is an added advantage.
Bachelor's degree in Technology/Engineering (B.Tech/B.E.) or equivalent qualification.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data Science & Machine Learning - OtherEmployement Type: Full time