JOHN HANDLEY DERECHO
AI Engineer
Profile
AI engineer experienced in client-facing delivery, with a track record in production LLM systems, agentic AI, and Retrieval-Augmented Generation (RAG) in Python. Comfortable owning architecture end-to-end.
Languages: Spanish (native) · English (C2) · Swedish (A1) · French (A2)
Experience
Conversational voice AI agent · Python (Google API); Dialogflow, ElevenLabs
• Technical lead on production Spanish voice agents handling live inbound calls for a major national telecom (Dialogflow CX, ElevenLabs, Genesys, Google Cloud) and point of contact for client and domain experts
• Designed multi-flow conversational architecture and Python fulfilment services on Cloud Functions, including automated regression testing, increasing accuracy from 50% to 95%
Graph-RAG Platform · Python (LangGraph, Neo4j, Qdrant, Docker)
• Lead engineer on an enterprise Graph-RAG platform; boosted answer accuracy from ~30% to ~90% in 2 months through hybrid KG + embedding retrieval, improved query parsing and dynamic Cypher generation
• Designed a modular LangGraph multi-agent architecture with schema-aware Neo4j resolution, fuzzy/lexical matching and deterministic + LLM tooling; fully containerised with Docker
AI Agents — Virtual Assistants · Python, JS (LangChain, CrewAI, FastAPI)
• Built production semantic-search assistants for client ticket retrieval, cutting lookup time from 5–10 min to seconds and saving 100+ hours/month
Skills
Programming: Python (OOP, async, APIs), Bash/Linux, Git, CI/CD, architecture prototyping
AI & ML: LLMs (OpenAI, Ollama), LangGraph, CrewAI, RAG, NLP (spaCy, HuggingFace), Dialogflow CX, scikit-learn, PyTorch, MLFlow, LLM-as-a-Judge, MCP
Data & Storage: Neo4j, Cosmos DB, PostgreSQL, SQL, ETL pipelines, vector search (Qdrant, Azure AI Search)
Cloud & Infra: Azure (DevOps, Key Vault, AI Search, Blob), Docker, Google Cloud, Agile (Scrum, Jira); Linux
Certifications: Azure AI Fundamentals (AI-900); ElevenAgents Certified Partner Deployment Engineer
Projects
Antarctic Sea Ice Concentration from Sentinel-2
Python · PyTorch · rasterio
Weakly supervised regression (fine-tuned ResNet18): trained against coarse AMSR2 sea ice concentration labels (12.5 km) to predict per-patch SIC from Sentinel-2 L2A imagery at 10 m (2.56 km patches), resolving sub-grid-cell ice structure beyond AMSR2's native resolution.
Master's Thesis: Trained transformer models on 1.8M+ Wikipedia segments to analyse the effect of training data on neural machine translation quality
Education
BSc Computer Science (in progress) · Universitat Oberta de Catalunya · Online2026 – Present
MSc Language Analysis & Processing (spec. Machine Translation) · Universidad del País Vasco2020 – 2022
BSc Translation & Interpreting · Universidad Complutense de Madrid2016 – 2020