ML Engineer based in London with an MSc in Artificial Intelligence and Adaptive Systems from the University of Sussex. I build applied AI and machine-learning systems—from data and model experiments to usable developer and product experiences. My research focused on reinforcement learning, specifically asymmetric forgetting in dual-value RL architectures for non-stationary environments.
Previously the sole Frontend Developer at MedAll, where I owned the frontend and most product-interface design for a medical learning platform. I bring both research depth and production engineering discipline to everything I build.
Currently exploring: MCP (Model Context Protocol) for ML operations · Multi-agent systems with LangGraph
| Evidence | What it means for a hiring team |
|---|---|
| MSc AI, University of Sussex (2025) | Research-led ML foundation, including a PyTorch reinforcement-learning dissertation. |
| Production product delivery | Sole Frontend Developer at MedAll (2020–2022): owned frontend delivery and most UI/UX design for a medical-learning product. |
| Hackathon result | 3rd place, 39 teams / 150 participants at Hack Night London (Tessl, May 2026) for CareerMentorGraph, built solo in one evening. |
| Problems I have solved | Trustworthy PDF question answering with citations; explainable graph-based career planning; no-code ML workflows; local-first AI tools and health-product prototypes. |
| Best technical evidence | LocalDocRAG · CareerMentorGraph · Crop Yield Forecasting · Asymmetric Forgetting RL |
Recruiter quick links: LinkedIn · Email · Featured repositories
Machine Learning & AI
MLOps & Infrastructure
Also Proficient In
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Original research from my MSc dissertation A dual-value reinforcement learning architecture that separates reward and punishment learning signals with asymmetric forgetting rates. Improves adaptation in non-stationary environments where optimal strategies shift over time.
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AI career guidance as a knowledge graph Maps skills, gaps, and learning paths using graph reasoning and LLM-powered analysis. Surfaces personalised upskilling routes based on role targets and current skill state.
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PDF Q&A with source citations, runs locally Upload PDFs, ask questions, get page-cited answers. Runs fully offline on a Raspberry Pi. LangChain retrieval pipeline with pgvector, FastAPI backend, React frontend, Docker Compose deployment.
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PyTorch MLP across 165 countries and 102 crops Multi-source climate, soil, and land-cover data fused into a single MLP trained on 52K+ samples. R squared of 0.9452, Pearson r of 0.9681. One-year-ahead forecasts with per-country breakdown.
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Asymmetric Forgetting in Dual-Q Reinforcement Learning — MSc Dissertation, University of Sussex (2025)
Developed a dual-value RL model separating reward and punishment learning signals with asymmetric forgetting to improve adaptation in non-stationary environments. Evaluated in a custom 2D grid-world with shifting reward locations. Analysed results in the context of computational forgetting, stability-plasticity trade-offs, and implications for adaptive AI systems.
🎓 MSc Artificial Intelligence and Adaptive Systems — University of Sussex, 2025
🎓 BSc Computer Science — Amirkabir University of Technology (Tehran Polytechnic), 2020
📜 Machine Learning Specialization — Stanford University & DeepLearning.AI
📜 CS50: Introduction to Computer Science — Harvard University
Open to ML Engineer, AI Engineer, and LLM/Agentic AI roles in London.
If you're looking for someone who builds production AI systems, not just notebooks, let's talk.