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Metro Bangalore, common data role and mid-level experience attract many applicants despite SAP/ML specialization.
Requires SAP-specific knowledge plus data/ML skills, making background partially transferable.
Multiple mandatory skills across data engineering, ML, SAP and cloud enforce strict technical filters.
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Develop and deploy AI/ML models for classification, NLP, anomaly detection, forecasting, and enterprise AI use cases.
Design and implement scalable data pipelines using Databricks, PySpark, Delta, MLflow, and Azure Data Factory.
Collaborate with business stakeholders to translate requirements into AI-driven technical solutions and implement MLOps practices including model validation, monitoring, and retraining.
Strong hands-on experience with AI/ML development using Python, Scikit-learn, TensorFlow, or PyTorch.
Experience building and deploying ML models for classification, NLP, anomaly detection, etc.
Hands-on experience with Databricks, PySpark, Delta, MLflow, and Azure Data Factory for data pipelines.
Experience with SAP data models, SAP BDC, SAP Analytics Cloud, and SAP data migration/transformation workflows.
Proven ability to work across AI/ML development, data engineering, SAP data integration, and cloud/MLOps platforms in enterprise settings.
Comfortable working with cross-functional and distributed teams to deliver production-ready AI solutions.
Experienced in driving PoCs, rapid prototyping, and translating complex business problems into scalable technical implementations.