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Tier-1 employer plus a common fullstack title and broad skillset produce medium competition.
Core engineering, data and ML skills are transferable but healthcare domain preference increases sensitivity to medium.
Many mandatory technical stacks and specialized Azure/ML/Databricks requirements create high shortlisting strictness.
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Develop and maintain scalable data engineering solutions and enterprise-grade applications using Python, React JS, SQL Server, Azure services including Databricks and Azure ML.
Design and implement Lakehouse architectures, real-time and batch data pipelines, and AI/ML-driven automation and triaging systems leveraging Azure OpenAI and ML services.
Own end-to-end responsibilities including architecture, design, development, testing, deployment, and production support of data ingestion frameworks, REST APIs, and monitoring systems.
Undergraduate degree or equivalent experience.
Experience designing and architecting large-scale, enterprise-grade applications with modern best practices for scalability, security, and reliability.
Hands-on experience with Python, React JS, SQL Server, Azure environment, Azure Databricks, PySpark, Spark SQL, Delta Lake, and deployment of AI/ML solutions on Azure (Azure OpenAI, Azure ML).
Work Experience Required: Not explicitly mentioned in the JD.
Proven expertise in building and optimizing enterprise data pipelines and Lakehouse architectures with strong knowledge of Azure data services.
Experience in designing scalable AI/ML systems including LLMs, RAG, and agentic AI architectures with practical implementation in Azure ecosystem.
Ability to independently manage end-to-end development lifecycle including architecture reviews, code reviews, deployment and production support in an enterprise setting.