Building agentic AI platforms for national agencies: MCP servers, tool-calling analysts, data collectors, forecasting models, dashboards.
Background: 6+ years across R&D, application development, and research, including LLM agents with RAG and ML shipped in a commercial product.
AquaScope ⭐ 30Explore river records and run reproducible water analyses in your browser or in Python, with an agent on top.
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Agronaut ⭐ 5Open-source agriculture assistant you run on your own machine: a multimodal tool-calling agent over a deterministic, cited engineering core.
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A differentiable research twin of Taiwan's Choushui alluvial fan: pumping policy in, groundwater heads and land subsidence out, gated against held-out data at every stage.
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Speech recognition and translation for Mooré, a language of ~8M people and almost no training data.
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paper-agent ⭐ 8 · A Claude Code plugin that turns the agent into a disciplined manuscript collaborator. Six strict modes (draft, review, revise, proofread, audit, format), Semantic Scholar MCP citation resolution with no API key, anti-fabrication guardrails, a number checker that traces every figure to its source, and a submission-ready |
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PyTorch · Hugging Face (transformers, PEFT, datasets) · LangChain · LangGraph · AutoGen · MCP · CUDA / bf16 ·
NVIDIA PhysicsNeMo · RAG & vector search (FAISS) · Ollama / vLLM · pandas · NumPy · SciPy · FastAPI ·
Django · PostgreSQL · Docker · GitHub Actions · pytest · Streamlit · Gradio
Agentic AI & tool use · LLM fine-tuning for low-resource languages · Evaluation, guardrails & AI safety in production ·
Physics-informed & scientific ML · Uncertainty quantification · AI as a digital public good
Open to conversations about AI/ML engineering roles and collaborations. LinkedIn · rachidouedraogo.com
Building AI for science · Taichung, Taiwan



