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Specialized Databricks+AI skillset reduces candidates but metro and senior title increase competition.
Databricks/PySpark skills transfer broadly, but specialized Databricks+LLM experience narrows candidate fit.
Many mandatory platform, Databricks and AI skills plus immediate availability requirement increase screening rigor.
Design and build data pipelines into Databricks using PySpark and PySQL from multiple data sources like SAP and web scraping.
Develop and deploy Databricks Apps, configure Azure and Databricks resources including authentication models, permissions, and CI/CD deployment pipelines.
Apply AI capabilities including FastAPI/React front-ends, prompt engineering, Retrieval Augmented Generation on Databricks, and implement AI safety guard rails.
Proficient in PySpark, PySQL, Databricks Unity Catalog permissions, and Databricks Apps development.
Experience with Azure resource configuration for Databricks deployments and CI/CD pipeline automation including security validations.
Familiarity with AI toolsets such as Claude Models/APIs, prompt engineering, and Retrieval Augmented Generation methods.
Work Experience Required: Not explicitly mentioned in the JD. Notice Period: Only immediate joiners preferred.
Experienced in integrating data pipelines and managing Databricks environments with a focus on security, permissions, and deployment automation.
Skilled in AI application development layered on Databricks platforms, including usage of language models and prompt engineering for domain-specific solutions.
Comfortable working individual contributor in agile sprint cycles collaborating with technical and non-technical stakeholders on minimal requirements.