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Tier-1 brand and mid-level manager title increase competition, but niche GenAI and Azure specialization limits applicant pool.
Role requires specialized Azure Databricks, MLOps, and GenAI expertise, making skills less transferable across industries.
Mandatory 6+ years plus extensive Azure, Databricks, MLOps, GenAI and vector DB tech stack enforces strict filters.
Own and drive the end-to-end Machine Learning lifecycle including data ingestion, feature engineering, model training, deployment, monitoring, and governance on Azure.
Architect, build, and operate enterprise-scale ML platforms and production-grade data pipelines using Azure Databricks, Spark, Kafka, and related technologies incorporating MLOps and automation.
Lead design and operationalization of Agentic AI and Retrieval-Augmented Generation (RAG) pipelines with multi-modal data, ensuring scalability, security, cost optimization, and enterprise AI adoption.
Graduate degree or equivalent experience required.
Minimum 6+ years of experience in Data Engineering.
Hands-on expertise with Azure Databricks, Spark, Delta Lake, Kafka, Event Hubs, Azure Machine Learning, MLflow, MLOps, CI/CD, Azure OpenAI, Generative AI, RAG, Agentic AI.
Proficient in Python, SQL, PySpark, GitHub Actions, Azure DevOps, Terraform/Bicep, and experience in distributed data processing and real-time data streaming on cloud-native platforms.
Experienced in managing complex enterprise-scale AI/ML platforms on Azure with strong operational and governance rigor.
Skilled in architecting and optimizing multi-modal data pipelines for advanced AI applications, including Agentic AI and RAG.
Demonstrates strong technical leadership and mentoring ability aligned with best practices in cloud-native AI engineering and data governance frameworks.