Hassan Hammoud
Squad Leader of AI
Skills
AIMachine LearningDeep LearningComputer VisionNLPGen AILLMLangchainJupyterPythonML OPSCI/CDDockerAWSBackendREST APISQL
Domains & Expertise
FintechMLOpsDeep LearningAgentic AI
Bio
AI Engineer & Researcher with 5+ years of hands-on experience building, deploying, and scaling production-ready AI systems. Strong background in Computer Vision, NLP, and Generative AI, with practical expertise in foundation and multimodal models. Experienced across the full AI lifecycle, including model training, fine-tuning, MLOps, deployment, monitoring, and optimization, using tools like PyTorch, TensorFlow, Hugging Face, LangChain, and FastAPI, along with cloud-based deployments and clean technical documentation.
Experience
AI Engineer
Remote Stations
Dec 2024 - Present
- • Designed an intelligent retrieval agent to automate extraction and summarization of SEC Edgar filings and financial data to support internal research, compliance, and decision-making.
- • Developed an end-to-end solution combining Azure Copilot Studio, Power Automate, LangChain, and Azure OpenAI, including custom connectors to fetch submissions and facts, dynamic routing logic, and summarization pipelines.
- • Enabled teams to retrieve and analyze complex SEC data in under 30 seconds, automating 70%+ of manual document.
AI Engineer
fixTman LLC
Dec 2023 - Nov 2024
- • Build and deploy production-ready computer vision AI systems end to end.
- • Handle the complete AI lifecycle, including data collection, cleaning, annotation, and preprocessing.
- • Develop image-based deep learning models using PyTorch, TensorFlow, and scikit-learn.
- • Manage model training, fine-tuning, evaluation, and performance optimization.
- • Deploy and scale models on AWS EC2 and Amazon SageMaker.
AI Research Assistant
University of Nebraska at Omaha
Mar 2023 - Nov 2023
- • Proposed a novel method by using multi-model CLIP for learning invariant image features guided by text to improve watermark robustness across transformations; published at IEEE MIRP 2025.
- • Worked on the development of a CLIP-based sparse positive multilabel learning method for open-world segmentation using only positive supervision.
- • Developed a robust image watermarking method using CLIP-based text-guided invariant features and feature space optimization. Gradient-based embedding was employed with data augmentation to ensure imperceptibility and resistance to distortions.
- • Used the Common Voice Accent data set and fine-tuning the Wav2Vec model to enhance its performance in recognizing diverse accents, achieving significant improvements in classification accuracy.
AI Engineer
Beyon Money
Oct 2019 - July 2021
- • Developed generalized AI agents for Google Gemini's training and evaluation ecosystem.
- • Designed modular, multi-agent architectures for prompt generation, tool integration, and adaptive decision- making.
- • Built automated evaluation pipelines to assess model reasoning, accuracy, and consistency across diverse tasks
- • Oversaw development of features like integration with payment systems.
- • Utilized Python, Autogen, LangChain, and Gemini APIs to enhance scalability, performance, and reliability.
Associate Data Scientist
Atomcamp
Feb 2017 - Sep 2019
- • Improved accuracy of multi-object tracking algorithm by optimizing parameters through grid and cube searches.
- • Utilized AWS Batch to run large scale experiments via Boto3
- • Built a Bokeh App to simulate incoming workload for Hudl's team of sports analysts leading to changes in staffing
- • Used Python to build an expected goals model based off player tracking and event data from soccer matches
- • Worked on chatbot development for the website and integrating it in the CRM. Also taught and mentored a batch of bootcamp students on modules of machine learning, Deep learning and NLP. All modules included hands on projects and live sessions.
Education
Bachelor's of Science in Computer Science
NUST
2015 - 2019