نبذة عني
AI Engineer with experience in developing and deploying agentic document intelligence pipelines
and Retrieval-Augmented Generation (RAG) platforms on Azure Databricks. Proven ability to fine-tune large
language models, e…
AI Engineer with experience in developing and deploying agentic document intelligence pipelines
and Retrieval-Augmented Generation (RAG) platforms on Azure Databricks. Proven ability to fine-tune large
language models, engineer preprocessing pipelines for complex data, and optimize real-time event delivery,
maintaining stringent latency SLAs. Skilled in Python, Azure, LangChain, and various LLM APIs, with a focus
on building compliance-aware automation solutions to drive business value for Boundless.
الخبرة
AI Engineer
Developed an agentic document intelligence pipeline on Azure Databricks, leveraging Gemini-powered OCR and entity extraction to process over 800 bank statements daily across 6+ banking formats, achieving a manual correction rate below 5% and accelerating credit decisioning while enhancing loan approval quality. Engineered a Medallion Lakehouse architecture on Azure Databricks using Delta Live Tables and Auto Loader; implemented the BSI Metrics Engine on Gold datasets with the Mosaic AI Agent Framework, enabling real-time financial health scoring and compliance analytics. Optimized real-time event delivery through a Redis-cached serving layer and Delta Sharing APIs, successfully maintaining 100 ms latency Service Level Agreements (SLAs) across 3+ downstream services, handling over 80,000 monthly events. Constructed a multi-provider Retrieval-Augmented Generation (RAG) platform (LangChain + Mosaic AI on Azure Databricks) featuring hybrid retrieval and sub-200 ms response times; orchestrated agents via MCP-driven control with constitutional AI guardrails for comprehensive MCA tracking and compliance-aware automation.
ML Intern
Fine-tuned Mistral-7B-Instruct-v0.2 using QLoRA on a specialized corpus of First Information Reports (FIRs), charge sheets, and court judgments; developed the data pipeline from raw ingestion to instruction-tuned training pairs, resulting in a 40% acceleration in case processing and over 55% precision in identifying corruption patterns. Engineered a Hinglish-aware preprocessing pipeline for code-switched FIRs, incorporating custom tokenization and transliteration normalization, which improved accuracy by 18% compared to the multilingual baseline; validated performance against more than 500 annotated passages.