Aditya Iyengar

Aditya Iyengar

Technology Lead

QuanHack Solutions

Aditya is a Certified Microsoft Data Engineer and Technology Lead at Quanhack, driving AI-powered innovation in software development. Specializing in Azure cloud engineering and Databricks administration, Aditya designs and manages robust cloud infrastructures and high-performance data platforms. Their expertise includes building end-to-end ETL solutions across Azure and AWS, working with tools like ADLS Gen2, Synapse Analytics, Azure Data Factory, and PySpark. Aditya also excels in InfraOps, deploying Azure Virtual Desktop environments and agile workflows. With a strong focus on compliance, they ensure adherence to GMP/GDP through rigorous validation protocols (IQ, OQ, PQ). Passionate about automation and operational excellence, Aditya transforms complex data challenges into scalable, secure, and value-driven business solutions.

In today's data-driven world, extracting meaningful insights quickly is paramount. Our AI analytics platform redefines this process by harnessing the transformative power of Large Language Models (LLMs). Beyond traditional data analysis, our innovative accelerator, QLytics, leverages LLMs to seamlessly convert your complex legacy queries into optimized, cloud-native code for platforms like Databricks and Snowflake. This integration not only accelerates your migration to the cloud but also democratizes data access, allowing users to interact with data using natural language, summarize vast datasets, and uncover hidden patterns with unprecedented ease and speed. Experience a new era of intelligent data analytics, where insights are just a conversation away.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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