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Data transformation and preprocessing Big Data Engineers apply algorithms and transformations to raw data, converting it into structured formats suitable for analysis and preparation for downstream applications.
As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation. Data quality also works hand in hand with datagovernance. in Computer Engineering from Bosphorus University in Istanbul. How does this all tie into AI/ML?
Data Integration and ETL (Extract, Transform, Load) Data Engineers develop and manage data pipelines that extract data from various sources, transform it into a suitable format, and load it into the destination systems. Data Quality and Governance Ensuring data quality is a critical aspect of a Data Engineer’s role.
Because of this, they will be required to work closely with business stakeholders, data teams, and even other tech-focused members of an organization to sure that the needs of the organization are met and comply with overall business objectives.
Exploring technologies like Data visualization tools and predictive modeling becomes our compass in this intricate landscape. Datagovernance and security Like a fortress protecting its treasures, datagovernance, and security form the stronghold of practical Data Intelligence.
I have experience designing scalable data pipelines, building robust APIs, and integrating AI-driven solutions. I hold a Master’s in ComputerScience and have published research in AI. Previously, I worked at Goldman Sachs, where I contributed to high-performance systems handling billions in daily transactions. I love software.
Strong background in ComputerScience. Experience integrating AI/ML models into production systems (LLMs, transformers, fine-tuning, etc.). Strong system design, datamodeling, and architectural thinking. Hands-on experience with FastAPI (or comparable modern Python web frameworks).
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