Ahmed – AWS, Python, Terraform

Modern data warehouses face several critical challenges, including understanding data lineage, identifying data silos, and interpreting complex transformations in ETL processes. Existing systems, including those leveraging LLMs, fall short of addressing these challenges due to a lack of grounding in the structured relationships inherent in data warehouses.

• Contributed to the PyTorch Geometric (PyG) open-source framework by addressing and resolving GitHub issue #9839;
• Implemented targeted bug fixes and performance enhancements for graph neural network data structures;
• Wrote comprehensive unit tests to ensure code stability, regression prevention, and cross-version compatibility;
• Collaborated with core maintainers and open-source contributors through code reviews and technical discussions;
• Optimized data processing workflows and pipelines for graph-based machine learning models.

An AI-driven analytics platform supporting large-scale regulatory and risk-analytics use cases. The product leverages scalable data workflows and Delta Lake on Databricks to process and analyze complex datasets, enabling reliable analytics for regulatory and risk-management needs.

• Architected AI-driven analytics solutions;
• Optimized Delta Lake workflows on Databricks to support large-scale regulatory and risk-analytics use cases;
• Designed scalable data-processing workflows for complex analytics requirements;
• Improved data workflows to support reliable regulatory and risk-analysis processes.

A secure, AI-powered decision engine that combines LangChain and LangGraph agents with private LLMs to automate complex decision-making workflows. The product supports controlled, end-to-end processing of decision logic while keeping sensitive data and AI processing within a private environment.

• Architected and delivered an end-to-end decision engine;
• Combined LangChain/LangGraph agents with private LLMs for secure, automated decision-making;
• Designed agent workflows to support complex decision-making processes;
• Integrated private LLM capabilities into automated decision workflows;
• Structured the decision engine for secure and reliable end-to-end processing.

Architected core generative AI technology and optimized massive-scale data pipelines for major enterprise clients, including Zendesk. Developed GPT-based AI assistants in Snowflake Cortex that enable natural language SQL generation to deliver automated, actionable business insights.

• Architected core generative AI technology;
• Led engineering teams to optimize massive-scale data pipelines for major enterprise clients, including Zendesk;
• Developed GPT-based AI assistants in Snowflake Cortex to enable natural language SQL generation and deliver automated, actionable business insights.

A fraud detection and real-time data processing solution designed to identify suspicious activity and support high-volume analytics. The product combines Azure ML with Delta Lake and Apache Spark to process streaming data at scale and enable timely, data-driven fraud detection.

• Developed robust fraud detection systems to identify and mitigate suspicious activity;
• Developed high-throughput streaming data pipelines utilizing Azure ML, Delta Lake, and Apache Spark to process large-scale data;
• Optimized data processing workflows to support scalable fraud detection and real-time analytics;
• Integrated machine learning capabilities into data pipelines to support automated fraud analysis;
• Improved data processing efficiency and reliability for high-volume streaming workloads.

An NLP-based entity resolution solution designed to improve master data management by identifying and matching records that refer to the same real-world entities. The product uses BERT embeddings and Random Forest models within production ETL pipelines to support scalable and reliable data matching.

• Designed production ETL pipelines for NLP-based entity resolution;
• Utilized BERT embeddings and Random Forest models to enhance master data management;
• Developed scalable data processing workflows for entity matching and resolution;
• Integrated NLP-based models into production ETL pipelines for automated data processing;
• Optimized data workflows to support reliable and consistent master data management.