About Me

I build multi-agent AI systems for regulated industries, where “it works” isn’t enough and every decision needs a paper trail.

Current focus: AI orchestration, RAG architectures, and agent memory systems. Recent work includes a multi-agent documentation system achieving 100% source fidelity and a 3-tier memory architecture that learns across sessions.

5+ years in quantitative modeling taught me what production-ready actually means: auditable, explainable, and built to survive scrutiny. I bring that discipline to AI systems.

💼 Focus Areas

  • AI Orchestration & Multi-Agent Systems: LangGraph workflows, agent memory, dynamic routing, and audit trails for production deployment.
  • Model Development: Forecasting, scenario analysis, and credit risk modeling using Python, SAS, and SQL in production-level environments.
  • MLOps & Cloud Deployment: End-to-end pipelines with Docker, MLflow, FastAPI, AWS, and GCP.
  • RAG & LLM Engineering: Zero-hallucination architectures, schema validation, retrieval pipelines, and source-grounded generation..
  • Scalable Data Workflows: Developing modular, reproducible analytics using PySpark, partitioned data lakes, and serverless/cloud-native deployment for monitoring and performance.
  • Telecom & Network Analytics: Anomaly detection, churn prediction, and 4G/5G KPI monitoring with multi-cloud deployment.
  • Behavioral Analytics: Simulating segmentation, churn, and engagement modeling use cases across diverse industries.

📚 Academic & Research Background

Educated in the U.S. and U.K., I’ve published peer-reviewed research on foreign investment and economic policy. My academic rigor informs my approach to real-world data challenges. I believe the greatest value lies not in a perfect model, but in a model you can trust, explain, and maintain. Beyond data, I’m drawn to storytelling, visual communication, and exploring how complex ideas become intuitive.

🌍 Languages & Interests

Fluent in English, Spanish, and Portuguese; advanced in French and Italian. I enjoy exploring languages, data visualization, and refining communication through storytelling.

🔗 Connect

📬 Newsletter: Exit Code 2 | AI Under Audit
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🛠️ Core Tools & Skills

Languages & ML:
Python • scikit-learn • SQL • PySpark • XGBoost • Prophet • SHAP • R • Stata • SAS

AI & LLM Systems: LangGraph • Retrieval-Augmented Generation (RAG) • LangChain • OpenAI API (GPT family) • Claude • Embeddings & Vector Stores (Chroma, FAISS)

Cloud & MLOps:
AWS (S3, SageMaker, Lambda) • GCP (Cloud Run, BigQuery) • MLflow • Render • Docker • GitHub Actions (CI/CD)

Data & Workflow:
Parquet • DuckDB • Neo4j • Pipeline design (Bronze → Silver → Gold)

Visualization & Apps:
Streamlit • Tableau • Plotly • Matplotlib

Other:
GIS (Certified)