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Hashem Farhan

@hashemfarhan

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Machine Learning Engineer at Factua who built an LLM application that increased qualified M&A leads by 35%.

United States

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At Factua, I built a production Python and LangGraph application combining web scraping with LLM reasoning, increasing qualified M&A leads by 35%.

I also developed a customer churn model using pandas, scikit-learn, and XGBoost that reduced churn by 7%. I built production ML pipelines on AWS to support data processing, inference, monitoring, and evaluation.

At MIT, I built Python and SQL pipelines to process medical eICU data and evaluated machine learning models to predict post-transfusion hemoglobin response. I used statistical analysis to interpret predictions and identify key drivers of transfusion response.

In internships at Jawaker, Maqsam, and Acacus Group, I implemented and evaluated NLP, LLM, RAG, and computer vision systems for production use. This work included reducing moderation workload and inference response time at Jawaker, building enterprise knowledge-base querying pipelines at Maqsam, and developing a vehicle and license-plate OCR pipeline at Acacus Group.

Experience

Work history, roles, and key accomplishments

Current

Machine Learning Engineer

Current

Factua

Jan 2026 - Present (9 months)

Built a production Python/LangGraph application using web scraping and LLM reasoning, increasing qualified M&A leads by 35%. Developed a customer churn model using pandas, scikit-learn, and XGBoost, reducing churn by 7%.

Python LangGraph Web Scraping LLM Pandas scikit learn XGBoost AWS Sagemaker Amazon SQS DynamoDB ML Pipelines

Machine Learning Researcher

MIT

Apr 2025 - Dec 2025 (8 months)

Built Python/SQL pipelines connecting to GCP BigQuery to process medical eICU data. Developed and evaluated scikit-learn (LightGBM) against deep learning models using cross-validation to predict post-transfusion hemoglobin response.

Python SQL BigQuery scikit learn LightGBM Deep Learning Cross Validation Statistical Analysis SHAP

Machine Learning Engineer Intern

Jawaker

May 2025 - Aug 2025 (3 months)

Implemented NLP and LLM methods from research papers using PyTorch and Hugging Face, adapting and benchmarking models for production use. Developed evaluation datasets and reproducible validation pipelines to measure model performance, robustness, and behavior under adversarial inputs.

NLP LLM PyTorch Hugging Face Evaluation Evaluation Pipelines Python Multimodal ML Testing Automation Experiment Tracking

Machine Learning Engineer Intern

Maqsam

Jun 2024 - Sep 2024 (3 months)

Developed production RAG pipelines for natural-language querying over enterprise knowledge bases, from data ingestion through retrieval and inference. Evaluated LLM and retrieval methods using reproducible experiments and optimized GPU inference for production workloads.

RAG LLM Retrieval Python GPU Inference Data Ingestion API Development Deployment

Machine Learning Engineer Intern

Acacus Group

Jul 2023 - Oct 2023 (3 months)

Developed a production computer vision and OCR pipeline to detect vehicles and extract license plate text. Built modular FastAPI/PostgreSQL services for data processing and ML inference.

Computer Vision OCR fastAPI PostgreSQL Docker Cloud Infrastructure Python