Raymundo Navarrete
Senior individual-contributor Data Scientist / ML Engineer — equally comfortable building LLM and agentic systems or rigorous classical statistical models, and turning either into production. 10+ years of quantitative and ML work.
About
I'm a machine learning engineer and data scientist with 10+ years of experience building models and systems that go to production. My work spans FinTech credit modeling, government-contracted analytics and risk modeling, and autonomous AI agent systems. I hold a PhD in Applied Mathematics from the University of Michigan and have a particular interest in where rigorous quantitative methods meet modern LLM tooling.
I'm looking for senior ML/AI roles where I can apply LLM orchestration, production ML pipelines, and systems-level thinking to problems that matter. I build AI agents for fun: the system running this site is itself an autonomous overnight prototype builder powered by Claude.
Experience
- Own the full Google Cloud architecture end-to-end — GCS/BigQuery data infrastructure, backend engine and programmatic data API, cost tracking, and error alerting across multiple services.
- Build and deploy authenticated ML prediction services that run models on user payloads, persist outputs to GCS/BigQuery, and trigger downstream actions; trained against 1TB+ datasets.
- Run the MLOps layer: QA-to-production deployment pipeline with smoke tests, CI/CD on GitLab Actions and Cloud Build, A/B experiments with model versioning including client-specific variants, autoscaling and automatic crash recovery.
- Portfolio risk management across the full auto loan book, re-evaluating exposure as new performance data accumulates to inform credit strategy — plus AI-powered automated reporting and code review.
- Led end-to-end migration and automation of loan loss forecasting platform, delivering more reliable dashboards and reducing team workload by 2 days/month.
- Designed alternative-data second-look model for a flexible rent product, increasing approvals while maintaining stable default rates.
- Fine-tuned LLMs and built prompt-engineering pipelines for structured risk scoring against ambiguous, adversarial technical documents — including a chemical-synthesis risk-assessment engine for a government client.
- Led procurement of $1M EPA contract and managed cross-functional team to deliver a predictive risk assessment model on time and within budget.
- Developed state-of-the-art ML techniques improving the flagship underwriting product, increasing quarterly performance gains by 100% and enabling the team to hit quarterly goals.
- Conducted time-sensitive analysis to identify underperformance in personal-loan underwriting during economic turbulence; implemented corrections in production.
- Published multiple peer-reviewed papers on ML, graph neural networks, and dimensionality reduction in top journals (IEEE TVCG, Physica D, EJS).
- Best applied mathematics thesis award. Research in dynamical systems, delay embeddings, and time series prediction using kernel methods.
Projects
Press / to focus filter — type skills.
Publications
Let's Talk
Senior ML/AI engineering roles — especially where LLM orchestration and production ML systems meet real business impact. Roles where I can own the technical architecture and drive real results.
- Applied ML / risk and decision systems
- AI engineering / LLM pipeline design
- Senior data science (ML systems in production)