I’m a Machine Learning / AI engineer focused on building production-grade ML systems that connect data engineering, backend infrastructure, observability, and AI-native workflows. I bridge data science and software engineering to turn raw operational data into reliable, traceable AI solutions, with a strong emphasis on data quality, evaluation rigor, and evidence-based decisioning. Recently, I’ve worked on enterprise AI initiatives including SAP S/4HANA plant maintenance data integration for a large renewable-energy operator, an agentic anomaly-detection and observability platform for distributed services, and a credit-approval decision system that combines ML outputs with auditable business rules. I also develop NLP and LLM evaluation pipelines—designing multi-task, calibrated scoring systems and producing structured, testable artifacts suitable for integration into APIs and backend services.…

Rodrigo Donoso AI Developer

I’m a Machine Learning / AI engineer focused on building production-grade ML systems that connect data engineering, backend infrastructure, observability, and AI-native workflows. I bridge data science and software engineering to turn raw operational data into reliable, traceable AI solutions, with a strong emphasis on data quality, evaluation rigor, and evidence-based decisioning. Recently, I’ve worked on enterprise AI initiatives including SAP S/4HANA plant maintenance data integration for a large renewable-energy operator, an agentic anomaly-detection and observability platform for distributed services, and a credit-approval decision system that combines ML outputs with auditable business rules. I also develop NLP and LLM evaluation pipelines—designing multi-task, calibrated scoring systems and producing structured, testable artifacts suitable for integration into APIs and backend services.…

Available to hire

I’m a Machine Learning / AI engineer focused on building production-grade ML systems that connect data engineering, backend infrastructure, observability, and AI-native workflows. I bridge data science and software engineering to turn raw operational data into reliable, traceable AI solutions, with a strong emphasis on data quality, evaluation rigor, and evidence-based decisioning.

Recently, I’ve worked on enterprise AI initiatives including SAP S/4HANA plant maintenance data integration for a large renewable-energy operator, an agentic anomaly-detection and observability platform for distributed services, and a credit-approval decision system that combines ML outputs with auditable business rules. I also develop NLP and LLM evaluation pipelines—designing multi-task, calibrated scoring systems and producing structured, testable artifacts suitable for integration into APIs and backend services.

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Skills

Experience Level

Python
Expert
Data Science
Expert
Docker
Expert
API
Expert
Google Cloud
Expert
SQL
Expert
Large Language Model
Expert
PyTorch
Expert
Prompt Engineering
Expert
AWS
Expert
ETL
Expert
Redis
Expert
Web Design
Intermediate
ML
Intermediate
Kubernetes
Intermediate
Data Visualization
Intermediate
TensorFlow
Beginner
OpenStack
Beginner
Object Detection
Beginner
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Language

Spanish; Castilian
Fluent
English
Fluent

Work Experience

AI/ML Engineer — FS A SyncHub (Contractor) at South Geeks
July 1, 2026 - August 31, 2026
Architected an AI/ML mapping engine for a cloud-native pipeline migrating 10–15k maintenance records into SAP S/4HANA Plant Maintenance. Led data profiling (signal recoverability, language mix, completeness) and authored architecture proposals with ownership boundaries across SAP and full-stack teams. Performed feasibility analysis for multiple SAP PM classification/resolution targets across heterogeneous contractor datasets, and built a reproducible Python pipeline to profile XLSX/JSON/XML/PDF evidence by extracting text-code evidence and reconciling target-level signals. Defined an evidence framework separating runtime signals, validating mappings, and resolving unresolved cases prior to any model-performance claims. Implemented notebook-centric analysis with a modular Git/GitLab workflow producing repeatable, sanitized, and auditable outputs.
Senior ML / Back-End Engineer — Agentic Observability & Anomaly Detection Platform (Independent R&D) at Independent R&D Initiative
February 1, 2026 - July 31, 2026
Designed an industrial-grade POC combining ML, OpenTelemetry observability, distributed services, event-driven signals, and an agentic reasoning layer for incident analysis. Built two complementary ML pipelines: Isolation Forest for service-level anomaly scoring and HDBSCAN for temporal propagation clustering across simulated payment/checkouts/orders and Redis services. Transformed traces/spans/logs/metrics/latency/error rate/throughput/retry/dependency signals into structured, validated, feature-extracted, versioned ML datasets. Instrumented services with OpenTelemetry and used Redis Pub/Sub + Streams to decouple telemetry generation, detection, correlation, and agentic context construction. Implemented a skills-first agent layer that consumes compact context, evaluates evidence, supports root-cause hypotheses, applies confidence and policy gates, and produces auditable recommended actions, with containerized, testable components suitable for future Kafka/Flink/Kubernetes evolution.
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.
AI & Machine Learning Systems Developer — Credit Approval Decision Platform at Independent Project
September 1, 2025 - February 28, 2026
Planned and developed an end-to-end credit-approval decision system combining credit risk, fraud, and payoff models into a single auditable decision flow. Converted exploratory notebooks into production-minded training and inference scripts with versioned datasets, model artifacts, and internal benchmarks for model version comparisons. Built a policy-decider layer mapping model outputs and business rules into APPROVE/REVIEW/REJECT outcomes intended to integrate with BRMS-style workflows (e.g., Red Hat RH PAM). Containerized the workflow with Docker and prepared it for cloud-oriented deployment, including environment configuration, basic logging, and data contracts for downstream integration. Delivered multiple specialized XGBoost models and implemented an auditable ML+BRMS governed decision pipeline.
AI & Machine Learning Developer — IELTS Essay Scoring System at Independent Project
June 1, 2025 - September 30, 2025
Designed an automated IELTS essay scoring pipeline using a DeBERTa-based architecture to predict multiple traits (Task Response, Coherence & Cohesion, Lexical Resource, Grammar). Owned the full ML workflow including data cleaning, train/test splits, multi-head modeling, QWK/MAE evaluation, calibration, and aggregation into consistent band scores. Migrated from notebooks to scripted training and evaluation for reproducible experiments and easier integration into backend services or APIs. Ran experiments on Vertex AI and AWS SageMaker including data upload, training-job setup, logs, and artifact retrieval. Achieved strong agreement with human scoring and implemented temperature calibration and hybrid focal + cross-entropy training with appropriate stratified validation and early stopping.
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 Trainer, Prompt Designer & Annotator (Freelance) at Outlier / Alignerr
December 1, 2024 - Present
Evaluated LLM outputs and provided structured feedback for remote AI training workflows, improving practical judgment around LLM behavior, limitations, safety, grounding, and evaluation. Designed rubrics and prompt prototypes supporting quality dimensions such as instruction following, usefulness, safety, and groundedness.
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.
Independent Developer — Foundations & Portfolio at Self-directed
November 1, 2024 - March 31, 2025
Built a portfolio of web projects, created initial GitHub repositories, and practiced Python for data analysis, scripting, and machine learning fundamentals through self-directed learning and small deliverables.

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.
A.A. in Humanitites, Arts & Languages (Certificate of Completion listed) at City College of San Francisco
January 1, 2010 - January 1, 2010
B.B.A. in Accounting & Auditing at Universidad de Santiago de Chile (USACH)
January 1, 2002 - January 1, 2002
IBM Generative AI Engineering Specialization (Professional Certificate) at IBM Academy
January 1, 2025 - January 1, 2025

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
IBM Generative AI Engineering Specialization (Professional Certificate)
January 1, 2025 - October 12, 2026
Linux Foundation: Cloud Native Foundations (NCFC) — CI/CD best practices; SRE; OpenTelemetry instrumentation
January 11, 2030 - October 12, 2026
Confluent / IBM: Apache Kafka and Flink
January 11, 2030 - October 12, 2026
Microsoft Learn: Machine Learning and Applied AI series on Azure
January 11, 2030 - October 12, 2026

Industry Experience

Software & Internet, Financial Services, Education, Energy & Utilities, Professional Services
    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.

    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.