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Mid-level, popular data/AI role in Bangalore with broad skillset increases applicant competition.
Core data and ML skills transfer across industries, though ERP and supply-chain experience increases fit sensitivity.
Explicit 4–6 years, production project requirements, and specific data/ML tool experience create moderately strict filters.
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Design, build, and maintain scalable ETL/ELT pipelines ingesting data from SAP S/4HANA, Oracle, Salesforce, and third-party APIs into enterprise data lakes/lakehouses.
Develop and optimize data products using medallion architecture on platforms like Databricks, implementing data quality, lineage, and governance controls.
Build, deploy, and monitor ML models and GenAI/LLM-powered solutions, operationalizing them with MLOps and managing workloads on Azure, AWS, or GCP.
4–6 years of professional experience in data engineering, ML engineering, or applied AI roles.
Proven track record delivering at least 2–3 production-grade data or AI projects end-to-end.
Experience with data engineering and ML deployment on cloud platforms including Azure, AWS, or GCP.
Work Experience Required: 4–6 years in relevant data/AI engineering roles.
Hands-on expertise integrating enterprise ERP data (SAP S/4HANA, Oracle) using patterns like CDC, IDoc, OData, and APIs.
Experience with supply chain, procurement, or logistics analytics use cases such as spend analysis, demand forecasting, or supplier risk assessment.
Familiarity with advanced GenAI frameworks such as Copilot Studio or Agentic AI implementations.