Usman Haider
AI/ML & Data Solutions Engineer
New to Contra
Usman is ready for their next project!
Worked on an RLHF (Reinforcement Learning from Human Feedback) pipeline focused on dataset creation, data annotation, and model evaluation. My role involved designing and curating high-quality prompt datasets, reviewing AI-generated responses, and providing structured feedback based on accuracy, relevance, safety, and helpfulness. Contributed to improving model performance by ensuring consistent evaluation standards and high-quality human feedback for training alignment and refinement.
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22
Trained a DreamBooth LoRA model to generate high-quality, personalized image outputs with consistent subject identity across different prompts and styles. The project involved dataset preparation, image captioning, and fine-tuning diffusion models using LoRA for efficient training and deployment. The solution enables fast generation of customized visuals while preserving subject consistency, style control, and high fidelity, suitable for creative, branding, and content generation use cases.
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26
Fine-tuned a GPT-based language model to generate stories aligned with individual user writing styles. The project involved collecting and structuring user-specific writing samples, training the model to learn tone, vocabulary, and narrative patterns, and optimizing it for coherent long-form storytelling. The system can adapt to different authors’ styles, producing personalized and context-aware stories while maintaining consistency, creativity, and fluency across diverse prompts.
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31
Fine-tuned OpenAI Whisper model on domain-specific medical audio data to improve transcription accuracy for clinical and healthcare use cases. The project involved preprocessing medical speech datasets, handling noise and terminology challenges, and optimizing the model for improved recognition of medical vocabulary, accents, and context-heavy conversations. Delivered a robust speech-to-text system capable of producing highly accurate, structured transcriptions suitable for documentation, reporting, and downstream healthcare applications.
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36
Built an intelligent inventory management system that automates stock ordering using machine learning and AI agents. Leveraging an XGBoost-based forecasting model, the system predicts future inventory demand and proactively places purchase orders when shortages are detected. The backend is powered by Django, integrated with AWS-hosted datasets for scalability and real-time data access. AI agents handle autonomous procurement decisions, reducing manual oversight and streamlining supply chain operations for greater efficiency and accuracy.
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47
Developed a healthcare assistant application using Django (backend) and React (frontend). Users upload their medical records, and the system suggests medications based on historical data and symptoms. It also sends emergency alerts to nearby doctors via email for serious cases. PostgreSQL and OpenAI’s function calling feature were employed for data storage and automation.
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51