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| # Parsa Rouhi — Personal Knowledge Base | |
| ## Personal Summary | |
| My name is Amirparsa Rouhi (Parsa). I'm an AI/ML engineer with an MSc in Data Science & AI (Distinction, 2025) from Bournemouth University and a BSc in Computer Engineering. I'm currently based in the UK and actively seeking AI/ML engineering roles, open to relocation to London. | |
| ## Education | |
| - **MSc Data Science & AI** — Bournemouth University, 2025 (Distinction) | |
| - **BSc Computer Engineering** — (completed before MSc) | |
| ## Technical Skills | |
| - **Languages**: Python (primary), SQL | |
| - **ML/DL Frameworks**: PyTorch, TensorFlow, Hugging Face Transformers | |
| - **LLM & RAG**: LangChain, LangGraph, RAG pipelines, pgvector, FAISS, LoRA/PEFT fine-tuning | |
| - **Backend**: FastAPI, REST APIs | |
| - **Cloud & Deployment**: AWS, Docker, Hugging Face Spaces | |
| - **NLP**: Transformers, BERT, GPT-based models, Swin Transformer, sequence modeling | |
| - **Computer Vision**: Medical image analysis, radiology report generation | |
| ## Key Projects | |
| ### RADSAM Platform | |
| A production-grade radiology AI platform. Built an automated radiology report generation system using a Swin Transformer encoder paired with a Cerebras-GPT-1.3B decoder. Evaluated on the IU-Xray dataset. The project became the foundation for my MSc dissertation and a conference paper submission to IUI 2026. | |
| ### R2GenTransformer (MSc Dissertation) | |
| "A Lightweight Transformer Framework for Automated Radiology Report Generation." Focused on prompt engineering techniques with a Swin encoder + Cerebras-GPT-1.3B architecture on the IU-Xray dataset. Submitted to IUI 2026 conference. | |
| ### ParsaGPT | |
| A production-deployed conversational AI system. Built end-to-end with a custom RAG pipeline, demonstrating real-world LLM deployment skills. | |
| ### Multi-Agent RL Trading System | |
| Built a multi-agent reinforcement learning trading system using PPO (Proximal Policy Optimization). Explored deep RL for financial market modeling. | |
| ### Kaggle — Predictiva Competition (Pairwise Trading Agent Prediction) | |
| Progressed from a 57% baseline to ~89% validation accuracy through: | |
| - LSTM sequence modeling | |
| - Feature engineering | |
| - Deep learning ensembles (feedforward neural networks + rank-based ensembling) | |
| ### Cryptocurrency Token Pricing (Academic Research) | |
| Research project modeling Nash equilibrium dynamics and behavioral economics for token pricing. Used game-theoretic approaches. Targeting academic publication. | |
| ## Career Goals | |
| - Seeking AI/ML Engineering roles in the UK (junior to mid-level) | |
| - Open to industries: FinTech, HealthTech, EdTech, biotech, general AI product companies | |
| - Requires visa sponsorship for future work authorization | |
| - Preference for remote-friendly or London-based roles | |
| - Particularly interested in: LLM applications, RAG systems, production ML, NLP | |
| ## What Makes Me Stand Out | |
| 1. **Production experience**: RADSAM and ParsaGPT are deployed systems, not just academic exercises | |
| 2. **End-to-end ML**: I've done research (IUI 2026 paper), built systems, and deployed them | |
| 3. **Breadth + depth**: Medical AI, financial AI, LLMs, RL — across multiple domains | |
| 4. **Distinction-level MSc**: Graduated with top marks from a UK university | |
| 5. **Hands-on LLM expertise**: LoRA fine-tuning, RAG pipelines, multi-tool agents (LangGraph) | |
| ## Contact & Profiles | |
| - Open to being contacted by recruiters | |
| - GitHub: visible with active project contributions | |
| - LinkedIn: active profile | |
| - Based in UK, eligible to work (requires future sponsorship) | |
| ## Personality & Work Style | |
| - I enjoy bridging rigorous academic methods with practical, deployable systems | |
| - Strong interest in financial markets and AI applications in trading | |
| - Self-driven learner — completed competitive Kaggle challenges alongside MSc | |
| - Comfortable working in fast-paced, research-oriented environments |