Parham is available for hire
Hire ParhamParham Hamouni
Verified Expert in Engineering
Data Scientist and Developer
Toronto, ON, Canada
Toptal member since March 14, 2022
Bio
Parham is a senior LLM engineer and PhD researcher with 6+ years of shipping production AI systems across manufacturing, legal, and finance. At Toronto Metropolitan University, his PhD applies DPO and RLHF to temporal graph reasoning, achieving 83% pairwise accuracy and outperforming baselines by 12–15pp. In production, Parham works with RAG pipelines, fine-tuned transformers (FinBERT, Qwen, LLaMA-2), and full MLOps stacks. At Kraft Heinz, his RAG system cut troubleshooting time by 30%.
Portfolio
Adams Fence, LLC
Artificial Intelligence (AI), Computer Vision, Machine Learning, Python, APIs
Fog City Code Works
Python, Natural Language Processing (NLP), Large Language Models (LLMs), MLflow...
The Kraft Heinz Company
Artificial Intelligence (AI), Natural Language Processing (NLP)...
Experience
- Python - 8 years
- Deep Learning - 8 years
- Graphs - 8 years
- Natural Language Processing (NLP) - 8 years
- Artificial Intelligence (AI) - 8 years
- Statistics - 6 years
- Computer Vision - 6 years
- SQL - 6 years
Work Experience
AI Engineer
2025 - PRESENT
Adams Fence, LLC
- Built an end-to-end document intelligence pipeline for construction bid automation: ingested multi-page PDF/Bluebeam plan sets via agentic OCR and spatial layout analysis, eliminating manual data entry in RFP-to-quote workflows.
- Engineered a production-ready Streamlit application deployed on AWS, enabling estimators to process technical construction documents and extract structured project elements without manual review, reducing quoting time by an estimated 60%.
- Designed a layout-aware document parsing system using spatial context and page structure to accurately identify and extract domain-specific construction elements across heterogeneous multi-page technical plan formats.
Technologies: Artificial Intelligence (AI), Computer Vision, Machine Learning, Python, APIs
Senior NLP Engineer
2025 - PRESENT
Fog City Code Works
- Led development of a paragraph-level text classification system for finance interview transcripts (10 years of labeled data, 30-40 categories).
- Implemented end-to-end pipeline: data cleaning, transformer fine-tuning (FinBERT/Qwen), experiment tracking (MLflow), and evaluation (macro-F1, calibration).
- Extended the pipeline with an agentic RAG system in phase two, incorporating multi-stage retrieval and re-ranking, improving micro-F1 by 15% over the fine-tuned baseline.
Technologies: Python, Natural Language Processing (NLP), Large Language Models (LLMs), MLflow, Hugging Face
Senior Data Scientist
2022 - 2025
The Kraft Heinz Company
- Designed and deployed a retrieval-augmented generation (RAG) system using large language models (LLMs) to assist maintenance technicians, reducing troubleshooting time by 30%.
- Built a reactive maintenance prediction model that decreased unplanned downtime incidents by 25%.
- Developed an unsupervised object detection system for retail shelves using DINO, improving shelf recognition accuracy by 40%.
- Utilized a full MLOps stack: HuggingFace Transformers, FAISS vector store, LangChain, Docker, PyTorch, and MLflow for experiment tracking and model deployment on Azure.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), Large Language Models (LLMs), XGBoost, Computer Vision, Azure, MLflow, Retrieval-augmented Generation (RAG), Deep Learning, Unsupervised Learning, Hugging Face, LangChain, FAISS, Docker, PyTorch
Applied Research Scientist
2022 - 2022
Alexi
- Created synthetic QA pairs from Canadian legal documents to train a legal retrieval system, increasing retrieval accuracy by 10%.
- Developed a classifier to distinguish legal facts and principles in case law, achieving 92% classification accuracy and handling end-to-end pipeline development.
- Trained a self-supervised language model on the sentences that accurately assigned relative risk in the news to the companies, assuming that headers and paragraphs of 10-K forms have the same concepts.
Technologies: Artificial Intelligence (AI), Azure, AWS Command Line Interface (CLI)
Applied Research Scientist
2019 - 2022
Crater Labs Inc
- Trained a self-supervised language model on the sentences that accurately assigned relative risk in the news to the companies, using news about companies in the stock market and their relative 10-K forms in the SEC database.
- Improved edge pair prediction by more than 60 percent to 95 percent Hits@1 score by reframing a knowledge graph entity prediction problem as a sentence pair classification.
- Predicted the best price discount for a startup company with a regression problem approach using multi-relational graph embedding techniques to create relative features.
- Used question answering data scraped from the client's forum to create a dialog generation network based on GPT-2 with a persona context feature. Also applied spectral regularization, which improved the perplexity score.
- Handled manufacturing defect detection in highly imbalanced data. Using conditional GANs and image-to-image translation techniques augmented the data so the data imbalance was managed and the classifier overcame overfitting. Used Azure environment.
- Applied unsupervised image encoding techniques such as adversarial autoencoders and CNN models to find similar images in a proprietary photoshoot dataset. Used AWS environment.
Technologies: Python, Deep Learning, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT)
Project History
TF-MF: Improving Multiview Representation for Twitter User Geolocation Prediction
https://ieeexplore.ieee.org/document/9073316Applied Graph Neural Networks to geolocate Twitter users by combining linguistic and network signals, improving geolocation accuracy by 12% over prior state-of-the-art. This work was published at the IEEE/ACM ASONAM 2019 conference.
Impact-DPO: Predicting Scholarly Influence with Temporal Preference Alignment
https://github.com/parhamouni/impact-dpoApplied Direct Preference Optimization (DPO) to pairwise citation forecasting on temporal text-attributed graphs. Fine-tuned LLaMA 2 and Qwen 2.5 on 175,000+ citation pairs across CS and physics domains. Achieved 83% pairwise accuracy, outperforming SPECTER 2 and SVR by 12pp and zero-shot prompting by 15pp. Includes 3-phase causal interpretability analysis. Under review at ACM TIST.
Education
2023 - 2026
PhD Candidate in Electrical and Computer Engineering
Toronto Metropolitan University - Toronto, ON, Canada
2016 - 2018
Master of Science in Civil Engineering (Transportation Planning)
Concordia University - Montreal, QC, Canada
Skills
Libraries/APIs
Pandas, XGBoost, PyTorch
Tools
AWS Command Line Interface (CLI)
Languages
Python, SQL
Platforms
Unix, Azure, Amazon Web Services (AWS), Docker
Other
Deep Learning, Graphs, Natural Language Processing (NLP), Computer Vision, Statistics, Artificial Intelligence (AI), Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Retrieval-augmented Generation (RAG), LLM Fine-tuning, Reinforcement Learning, Machine Learning, MLflow, Unsupervised Learning, Hugging Face, Optical Character Recognition (OCR), LangChain, FAISS, Document AI/OCR, APIs, Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization
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