Saudi Arabia
Designed, fine-tuned, and implemented AI/ML models across a range of applications, including specialized classification tasks and the development of intelligent, multimodal, task-specific agents. Ensured model robustness and performance through systematic evaluation. Engineered and deployed advanced Retrieval-Augmented Generation (RAG) systems by building and managing vector databases populated with internal knowledge bases and proprietary data, enhancing contextual relevance and factual accuracy. Developed and maintained ETL pipelines to process and prepare structured and unstructured data for model training, fine-tuning, and inference. Managed the full machine learning lifecycle, from data preparation and model training to advanced optimization. Deployed scalable models and implemented model quantization for efficient, low-latency inference on local and edge devices, including AI PCs.
Designed, fine-tuned, and implemented AI/ML models across a range of applications, including specialized classification tasks and the development of intelligent, multimodal, task-specific agents. Ensured model robustness and performance through systematic evaluation. Engineered and deployed advanced Retrieval-Augmented Generation (RAG) systems by building and managing vector databases populated with internal knowledge bases and proprietary data, enhancing contextual relevance and factual accuracy. Developed and maintained ETL pipelines to process and prepare structured and unstructured data for model training, fine-tuning, and inference. Managed the full machine learning lifecycle, from data preparation and model training to advanced optimization. Deployed scalable, high-performance models on cloud platforms and implemented model quantization for efficient, low-latency inference on local and edge devices.
Built and maintained scalable ETL data pipelines on GCP using Airflow, orchestrating data ingestion, storage, and transformation workflows. Engineered and optimized SQL queries in BigQuery for large-scale data analysis, transforming raw data into actionable business insights. Developed and deployed containerized data processing applications and APIs on Google Kubernetes Engine (GKE) to support data-intensive workflows and enable data-driven services.
Designed and validated quantitative investment strategies for equity markets using machine learning models to forecast market volatility regimes and economic cycles. Applied targeted factor-based investment approaches (e.g., momentum, value, quality, low volatility) for dynamic portfolio rebalancing aligned with predicted market regimes. Built end-to-end frameworks for automated financial data ingestion, time-series analysis, and the computation of market signals and financial indicators to support regime detection and strategy execution. Developed and implemented systematic portfolio construction and dynamic asset allocation models, adjusting exposures across factor strategies based on ML-driven regime forecasts to enhance risk-adjusted performance. Conducted event-driven backtesting and performance attribution of regime-aware strategies, with a focus on robustness, drawdown control, and return consistency.
Managed and recorded daily financial transactions, ensuring accuracy and compliance with policies. Collaborated in the preparation and monitoring of the annual budget. Conducted internal audits to assess the accuracy of accounting records and the effectiveness of internal controls. Developed and maintained Excel spreadsheets to track income and expenses. Utilized formulas, pivot tables, and charts to analyze and clearly present financial data.