I bring a research-oriented yet practical approach — combining experimentation, optimization, and systems thinking — to develop scalable, interpretable, and high-performance AI solutions aligned with real-world applications. I hold strong fundamentals (ML, evaluation, calibration) + functional thinking (risk scoring, text quality scoring, feature classification, preventing/forecasting) to deliver robust solutions that mirror today’s industry needs.

Rodrigo Donoso Yáñez

I bring a research-oriented yet practical approach — combining experimentation, optimization, and systems thinking — to develop scalable, interpretable, and high-performance AI solutions aligned with real-world applications. I hold strong fundamentals (ML, evaluation, calibration) + functional thinking (risk scoring, text quality scoring, feature classification, preventing/forecasting) to deliver robust solutions that mirror today’s industry needs.

Available to hire

I bring a research-oriented yet practical approach — combining experimentation, optimization, and systems thinking — to develop scalable, interpretable, and high-performance AI solutions aligned with real-world applications.

I hold strong fundamentals (ML, evaluation, calibration) + functional thinking (risk scoring, text quality scoring, feature classification, preventing/forecasting) to deliver robust solutions that mirror today’s industry needs.

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Experience Level

Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate

Language

Spanish; Castilian
Fluent
English
Fluent

Work Experience

AI Trainer & Prompt Designer at Freelance, Remote
December 1, 2024 - Present
Independently authored and developed an interpretable multi-criteria NLP model using hybrid rules and transformers with stable cross-criterion performance and audit trail.
AI & ML Engineer at Inglidesk-Independent-Portfolio Project
March 4, 2025 - September 2, 2025
Remote project work for building advanced Generative AI; IELTS Essay Scoring System as an independent project. Independently authored and developed an interpretable multi-criteria NLP model: Hybrid rules + transformers with stable cross-criterion performance and audit trail.
AI & Machine Learning Developer at Inglidesk-Independent
September 2, 2025 - Present
Agentic Credit Approval System + Decision Platform (2025 – ongoing)>>>Architecture: Coordinator Agent (Block A) orchestrates Risk / Fraud / Payoff sub-agents; Decision Platform (RHPAM) encapsulates rules, decision tables, and governance, with a migration path to Kogito for event-driven microservices and ephemeral RHPAM where appropriate.

Education

Certificate at IBM Academy / AI Academy (Online Courses) - Class of 2025
January 11, 2030 - April 29, 2025
Complete IBM-certified program leading to AI and ML Engineering . Strong focus on Python and its ML/AI libraries and frameworks, namely: Pytorch, Tensor Flow, Pandas, Numpy, and the like.
Certificate at San Francisco City College
January 11, 2011 - January 27, 2013
Completed the entire Lower Division credit requiremts to get ready to transfer to upper division college. Certificate of completion: Mention in Humanities- Languages/French language.

Qualifications

Certificate of Completion
January 11, 2030 - January 27, 2026
Generative AI Engineering with Python
January 7, 2025 - April 29, 2025
Certificate of completion profesional program IBM Academy- Generative AI and MAchine learning Engineering

Industry Experience

Software & Internet, Financial Services, Education
    paper Agentic Credit Approval System + Decision Platform

    This project exhibits a Coordinator Agent (Block A) orchestrates Risk / Fraud / Payoff
    sub-agents y sync with a Decision Platform (RedHatPAM) that encapsulates rules, decision tables,
    and governance, with a migration path to Kogito for event-driven microservices and ephemeral RHPAM where appropriate.

    Why it matters: Design aligns with modern data products (low latency, backpressure
    tolerance, replayability), while BRMS provides explainability, policy,
    and audit required by stakeholders.

    paper IELTS Essay Scoring System

    An interpretable multi-criteria NLP model:
    Hybrid rules + transformers with stable cross-criterion performance and audit trail. Includes also optimization strategies for transformer-based models, exploring adaptive learning rates, gradient clipping, and mixed precision training to enhance performance in natural language processing applications.
    This is also a focused POC/MVP/ prototype system for implementing multi-task learning architectures using DeBERTa models, covering fine-tuning strategies, loss balancing, and evaluation metrics for complex NLP scenarios.