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Tier-1 brand, popular mid-level data role, and metro location increase competition.
Core data engineering skills are transferable, but healthcare/regulatory preference raises domain specificity.
Extensive mandatory technical stack (Databricks, Snowflake, Azure, LLMs, MLOps, Airflow) creates strict filtering.
Design, develop, and maintain scalable data pipelines and AI/ML solutions using Databricks, Snowflake, Apache Airflow, and Azure platform services.
Build and deploy production-quality Python code for data processing, APIs, AI services, and manage ML pipelines including MLOps, model monitoring, and governance.
Collaborate across teams to architect, implement, and troubleshoot AI-driven products, data engineering workflows, and ensure security, scalability, and cost optimization on Azure.
Graduate degree or equivalent experience.
Hands-on production experience with Databricks (Spark/PySpark), Snowflake, Apache Airflow, and Microsoft Azure cloud.
Experience with Python programming for data engineering, API development, AI/ML applications, and deploying AI services on Azure AI platforms.
Experience working with Large Language Models (LLMs), Generative AI, RAG architectures, vector databases, prompt engineering, plus AI governance and MLOps practices.
Experienced in end-to-end data engineering and AI/ML solution ownership in production environments using modern cloud services and frameworks.
Strong background integrating AI capabilities into enterprise data products with a focus on scalable, secure, and monitored deployments on Azure.
Familiar with advanced AI approaches including Agentic AI frameworks, intelligent workflow orchestration, and AI governance in regulated or complex domains.