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Mid-level role, known employer, Bangalore location, and broad data+AI skillset increase applicant density.
Core data and ML skills are transferable, though ERP and supply-chain preferences add domain specificity.
Explicit 4–6 years, mandatory 2–3 production projects, and specific tools/MLOps requirements raise filter strictness.
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Design, build, and maintain scalable ETL/ELT pipelines ingesting data from SAP S/4HANA, Oracle, Salesforce, and 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, monitor ML models and develop GenAI/LLM-powered solutions with operational MLOps practices across Azure, AWS, or GCP environments.
4–6 years of professional experience in data engineering, ML engineering, or applied AI roles.
Proven delivery of at least 2–3 production-grade data or AI projects end-to-end.
Experience with cloud platforms Azure, AWS, or GCP and related data/AI services (e.g., Databricks, ADF, Glue, Lambda, Kubernetes).
Work Experience Required: 4–6 years in relevant field.
Experience integrating and working with enterprise ERP data systems such as SAP S/4HANA and Oracle, including CDC, IDoc, and OData APIs.
Familiar with supply chain, procurement, or logistics analytics use cases (e.g., spend analytics, demand forecasting, supplier risk management).
Proficient in developing GenAI, LLM, or Agentic AI solutions including prompt engineering and working with tools like Copilot Studio or vector databases.