Gonzalo Romero, aka 𝔇𝔢𝔢𝔭ℜ𝔞𝔱 : AI engineer specialized in multimodal generative AI and applied AI systems. Passion for science since the age of 6 thanks to Carl Sagan. I love technology and information; They are the pillars of my daily life. Programming, studying and experimenting from my first computer. More than 12 years of continuous training between courses and certifications (20+), university and self-taught learning.

	Cortex — Corporate Knowledge Assistant Enterprise-grade RAG system built with hexagonal architecture, multi-tenant RBAC, PII-aware processing, and a full audit trail. Designed for regulated environments with strict data-leak prevention and production-grade observability. Stack: RAG · FastAPI · Qdrant · React · Redis · PostgreSQL · Docker · RBAC · DLP/PII
	MedX — Medical AI Assistant (Under Renovation) RAG-powered assistant for clinical document Q&A, integrating LangChain orchestration with the Kimi K2 model to deliver source-grounded responses for medical information retrieval and reasoning workflows. Stack: RAG · LangChain · Kimi K2 · Python · NLP · Vector Search
	CDR — Clinical Data Research · Agent Orchestration Coming Soon — 17/02/26 Multi-agent orchestration system for clinical data research. Autonomous agents collaborate to retrieve, validate, analyze, and synthesize clinical datasets and literature — designed for scalable, evaluation-driven research workflows in healthcare. Stack: Agents · Orchestration · LangGraph · RAG · Python · Evaluation Harness · Observability

⯈ GenAI & Applied AI Systems
• LLMs: GPT, Mistral, LLaMA, Falcon, Kimi, Qwen-VL
• RAG: LangChain, LangGraph, FAISS, Chroma, Weaviate, Pinecone
• Vector DBs: Qdrant, Milvus, ChromaDB
• Agents: ReAct, AutoGPTQ, OpenAgents
• Fine-Tuning: LoRA, QLoRA, PEFT, bitsandbytes
• Formats: GGUF, llama.cpp

⚙ AI Modeling & Training
• Frameworks: PyTorch, Transformers, scikit-learn, XGBoost
• Workflows: Training pipelines, quantization, distillation, eval metrics
• Vision: OpenCV, YOLOv8, CLIP, BLIP, ViT, Stable Diffusion, SDXL

✦ NLP & Data Intelligence
• Embeddings: SentenceTransformers, Cohere, OpenAI, HF models
• Processing: spaCy, NLTK, Regex, custom pipelines
• Graph Reasoning: GraphRAG, LangGraph memory

➤ Backend Engineering
• Languages: Python (Advanced)
• APIs: FastAPI, Flask, WebSocket
• Auth & Security: JWT, OAuth2, RBAC, Multi-Tenant
• DBs: PostgreSQL, MySQL, SQLite, MongoDB
• Observability: Logging, Prometheus, Grafana

⧉ MLOps & Deployment
• Hosting: Hugging Face, Ollama, Replicate, Triton, ggml
• CI/CD: GitHub Actions, Railway, Docker Compose, Make
• Cloud: AWS, GCP
• Monitoring: LangSmith, inference logs, custom APIs

🞄 Full-Stack & UI Dev
• Frontend: Gradio, Streamlit, HTML5, CSS3, JS, TailwindCSS
• UX: Prompt UIs, streaming interfaces, agents-as-apps
• Interfaces: Jinja2, Markdown rendering

Data & Analytics
• Tools: NumPy, Pandas, Seaborn, UMAP, Matplotlib
• Visualization: Embedding plots, attention maps, token analysis
• Workspaces: Jupyter, Google Colab

✔ End-to-End AI system design: from dataset to API & UI
✔ Multimodal & RAG-based systems in production
✔ Strong blend of backend engineering, ML, and DevOps
✔ Obsessed with tooling, benchmarks, and real-world reliability