Fadi Imani

• Designed and deployed an end-to-end FinTech solution on GCP, automating recurring business tasks and report generation through a machine learning model. Containerized with Docker and orchestrated via Apache Airflow, the pipeline handles monthly model retraining, inference, and automated report distribution to stakeholders. Eliminating manual overhead while ensuring consistent performance against evolving data. • Development and deployment of a recommendation engine based on reinforcement learning and neural networks, with complete infrastructure on GCP (Vertex AI, BigQuery, Cloud Run). • Design of an AI content generation system using prompt chaining and RAG to automatically leverage similar content, integrating a scoring system and generation explainability. • Implementation of a human-in-the-loop process for validation and continuous improvement, coupled with a self-learning mechanism through prompt tuning and approval/rejection pattern analysis.

• Project leader responsible for translating business requirements into data problems, while ensuring ethical and legal compliance in data usage. • Creation of a multi-tool agent based on an LLM, capable of interacting with APIs, Data Lakes, and databases. Deployment on GCP with a Google Chat interface. • Development of a fraud detection model with data ingestion from Databricks, deployed with Airflow and Kubernetes on GCP. • Development of a proprietary algorithm generating database-connected conversational agents with a single command, optimized for speed and reliability of responses, with RAG/CAG and deployment on AWS.

• Microscopy Image Processing: Developed specialized preprocessing pipelines to handle microscopy images of various microorganisms including bacteria, fungi, and protozoa. Implemented image enhancement techniques such as contrast adjustment, noise reduction, and normalization to improve visibility of cellular structures in different lighting and staining conditions. • YOLO-based Object Detection: Customized YOLO (You Only Look Once) architecture for real-time detection of multiple microorganisms in a single microscopy field. Fine-tuned the model to handle the unique challenges of microscopic imagery including overlapping organisms, varying cell sizes, and different growth phases. • Multi-class Classification System: Built deep learning classifiers using PyTorch to distinguish between different species and strains of microorganisms. Implemented transfer learning from pre-trained models and adapted them for biological image analysis, achieving high accuracy in identifying pathogenic vs. non-pathogenic organisms. • MLflow Integration: Established comprehensive experiment tracking using MLflow to monitor model performance across different hyperparameter configurations, dataset versions, and training runs. Created reproducible pipelines for model versioning, enabling systematic comparison of detection accuracy and classification metrics. • Database Architecture Optimization: Redesigned the biological image database to efficiently store and retrieve millions of microscopy images. Implemented indexing strategies for metadata (specimen type, magnification level, staining method) and optimized query performance for real-time model inference, reducing data access time by 70%.

• Data Collection & Processing: Built scalable data ingestion pipelines using PySpark to process large volumes of job postings, recruitment data, and labor market statistics from multiple sources. • Implemented distributed processing to handle millions of job listings and candidate profiles efficiently. NLP Feature Engineering: Leveraged spaCy for advanced text processing including named entity recognition (NER) to extract skills, job titles, and company names from unstructured job descriptions. Applied tokenization, lemmatization, and part-of-speech tagging to normalize text data and improve model accuracy. • Trend Analysis & Prediction: Developed time series models to forecast emerging job categories, in-demand skills, and salary trends across different industries and regions. Used statistical analysis in R to identify seasonal patterns and long-term market shifts. • Machine Learning Models: Implemented ensemble methods using Scikit-learn including Random Forests and Gradient Boosting to predict job market dynamics. Created classification models to categorize job postings and regression models to forecast demand for specific roles. • Multi-language Processing: Utilized Java for building robust data transformation services and API integrations with external job boards and recruitment platforms. Ensured seamless data flow between different components of the pipeline. • Insights & Visualization: Generated actionable insights about skill gaps, emerging technologies, and industry transformations. Created interactive dashboards to help HR professionals and policy makers understand labor market evolution and make data-driven decisions about workforce planning and training initiatives.

• Agricultural Data Analysis: Collected and processed comprehensive agricultural data including soil composition, weather patterns, irrigation schedules, fertilizer usage, and historical crop yields. Integrated multiple data sources from sensors, laboratory tests, and farming records to create a robust dataset for sugar content prediction. • Feature Engineering & Selection: Identified critical factors affecting sugar yield including rainfall patterns, temperature variations, soil pH levels, nitrogen content, and growing season length. Applied domain knowledge to create derived features such as accumulated degree days, moisture stress indices, and nutrient ratios that significantly improved model performance. • Predictive Modeling: Developed multiple regression models (Linear Regression, Random Forest, XGBoost) to predict sugar content and yield per hectare. Implemented cross-validation strategies specific to time-series agricultural data to ensure model reliability across different growing seasons and weather conditions. • Real-time Dashboard Development: Built an interactive Streamlit dashboard allowing agronomists and farm managers to input current field conditions and receive instant yield predictions. Included visualization components showing feature importance, prediction confidence intervals, and historical performance comparisons. • Model Interpretability: Integrated SHAP (SHapley Additive exPlanations) values to provide transparency in predictions, helping farmers understand which factors most influence sugar yield in their specific conditions. This enabled data-driven decisions about irrigation timing, fertilizer application, and harvest scheduling.