Diego Armando Salinas Lugo

Professional Summary

AI & Machine Learning Engineer, MSc Artificial Intelligence (Distinction). Ships complete systems — data pipeline, model, API, interface, cloud — not notebooks. Builds generative AI at SAP — 1st place at Innoweeks 2026 with ReflexIA — and founded ARMATUS, a production iOS training coach. Mechatronics background in signal processing, predictive analytics and real-time monitoring.

Technical Skills

Languages

Python, SQL, TypeScript, JavaScript, MATLAB, C

Machine Learning

scikit-learn, XGBoost, TensorFlow, Keras, Random Forest, Gradient Boosting, Support Vector Regression, Neural Networks, Feature Engineering, Hyperparameter Tuning, Cross-Validation (LOPO, LOSO, k-fold)

Generative AI & LLM

SAP Business AI, SAP AI Core, SAP AI Launchpad, SAP Generative AI Hub, GraphRAG, Model Context Protocol (MCP), ElevenLabs, OpenAI GPT-4o, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), Vector Databases (ChromaDB), Embeddings, Prompt Engineering, Multi-Agent Systems

MLOps & Deployment

FastAPI, REST APIs, Streamlit, Flask, Docker, Google Cloud Run, MLflow, Git, GitHub, Vercel, React Native (Expo), SAPUI5, SAP HANA Cloud, Redis, PostgreSQL, SQLAlchemy, Alembic, Supabase

Data, Evaluation & Domain

Pandas, NumPy, SciPy, NeuroKit2, Signal Processing (Butterworth, Welch PSD, PPG/HRV, vibration), Time-Series Forecasting, SHAP, LIME, ROC-AUC, Precision / Recall / F1, RMSE / MAE / R², Fairness Analysis, Predictive Maintenance, Healthcare & Clinical ML, Affective Computing, Robotics (PID, Fuzzy Logic), Industrial Automation

Spoken Languages

Spanish (native), English (professional), German (basic)

Professional Experience

1. AI & Machine Learning Engineer, SAP

   Jan 2026 — Present

   San Pedro Garza García, NL, Mexico

   • •Won SAP Innoweeks 2026 (1st place overall, Best Pitch, Best Popular Support; first Mexican winning team) as part of a 14-person Labs Monterrey sprint. Built the ReflexIA Gen AI microservice from scratch — STT, TTS and voice-to-voice (ElevenLabs), an MCP tool server, GraphRAG on SAP HANA Cloud, and a Joule-style SAPUI5 TypeScript UI.
   • •Tecmilenio validated the impact of replacing a 14-year-old 5+ platform budget-approval stack: $3.9M annual savings, 3,750 educator hours returned, equivalent to 518 student scholarships a year.
   • •Design and deploy generative AI that automates technical fault reporting on SAP Business AI, AI Core, AI Launchpad and the generative AI hub. Certified SAP Generative AI Developer (2026).

2. Founder & Machine Learning Engineer, ARMATUS (armatus.app)

   May 2026 — Present

   Monterrey, Mexico

   • •Founded and shipped a production iOS generative-AI training coach: onboarding, plan generation, live session tracking and progression, bilingual EN/ES.
   • •Architected a closed five-stage protocol (calibrate, plan, adapt, execute, evolve) that regenerates each week from logged sets, PRs and accumulated fatigue rather than serving a static template.
   • •Built a streaming API converting an athlete profile plus daily readiness signals (energy, sleep, time) into a dosed weekly plan with explicit rationale.
   • •Modelled endurance work as real systemic load so strength volume auto-adjusts behind a readiness ceiling; own the full surface from mobile client to API.

3. Data & AI Trainee, Interius

   Sep 2025 — Dec 2025

   San Pedro Garza García, NL, Mexico

   • •Delivered AI MVPs end to end — problem framing, feature engineering, model training, evaluation and deployment.
   • •Built contact segmentation across engagement, geography and channel activity, converting a flat list into targetable cohorts for personalized outreach.
   • •Shipped workload and efficiency diagnostics as containerized Streamlit services on Google Cloud Run for self-serve stakeholder access.

4. AI Researcher, University of Essex

   Nov 2024 — Sep 2025

   Colchester, United Kingdom

   • •Built an end-to-end emotion-recognition pipeline from PPG across 62 participants, reaching Pearson r = 0.5975 and RMSE 0.154 for continuous arousal, above the r ≈ 0.40 reported for comparable PPG-only work.
   • •Engineered 30+ HRV features across time, frequency (Welch PSD) and non-linear Poincaré domains with per-subject baseline normalization.
   • •Validated under leave-one-participant-out cross-validation with nested search, benchmarking Random Forest, XGBoost and SVR to prove generalization to unseen subjects.

Selected Projects

1. ReflexIA — SAP Innoweeks 2026 (1st place overall)

   SAP BTP, SAP HANA Cloud, GraphRAG, MCP, ElevenLabs, SAPUI5, FastAPI, Redis, TypeScript

   • •Built the AI core of a BTP-native approval workflow for Tecmilenio in a five-week sprint against five Brazilian teams: Gen AI microservice, MCP tools, GraphRAG on HANA Cloud, and a Joule-style voice/chat UI.
   • •University-validated outcome: $3.9M saved annually, 3,750 hours returned to educators, funding equivalent to 518 scholarships a year. Also Best Pitch and Best Popular Support.

2. Predictive Maintenance Platform — Remaining Useful Life

   rul-dashboard-368785016309.us-central1.run.app

   XGBoost, SHAP, FastAPI, Streamlit, LangGraph, OpenAI, MLflow, Docker, Google Cloud Run

   • •Predicted turbofan remaining useful life on NASA C-MAPSS at 16.74-cycle test RMSE, 12.33 MAE and R² 0.825, with SHAP exposing the driving sensor per prediction.
   • •Served it via FastAPI behind a Streamlit fleet dashboard, plus a LangGraph multi-agent layer answering natural-language questions and emitting PDF briefings.

3. Personal Research Assistant — Retrieval-Augmented Generation

   github.com/DArmandoSalinas/RAG

   FastAPI, LangChain, ChromaDB, OpenAI GPT-4o, text-embedding-3-small, Streamlit, PyPDF

   • •Built document question-answering that chunks, embeds and retrieves via MultiQueryRetriever, grounding every GPT-4o answer in ChromaDB and returning its source snippet.
   • •Indexed a 149-segment corpus and eliminated unsourced answers by making citation a hard requirement of the response contract.

4. Cardiovascular Risk Classification — Leak-Free Clinical ML

   github.com/DArmandoSalinas/HeartDisease-Predictor

   scikit-learn, HistGradientBoosting, SHAP, LIME, ColumnTransformer

   • •Classified coronary disease across 920 patients from four sites at 0.90 test ROC-AUC, 87.3% recall and 0.85 F1, tuning the threshold for recall since a missed case costs more than a false alarm.
   • •Prevented leakage by fitting the ColumnTransformer inside cross-validation, and exposed a transportability gap via Leave-One-Site-Out validation (mean AUC 0.79, worst site 0.70).

5. COAZON — Heart-Health Companion (in development)

   FastAPI, SQLAlchemy 2.0, PostgreSQL, Alembic, JWT, React Native (Expo), TypeScript, OpenAI, Railway

   • •Building a bilingual cardiac-care app that turns a prescription into a living plan — medication reminders, guided blood-pressure logging with clinical-zone trend charts, a deterministic red-flag alarm engine and a doctor-ready PDF report.
   • •Bounded the AI layer so it structures and summarizes but never diagnoses, prescribes or alters a dose, and made it degrade to fully manual without an API key so the app never depends on the model being available.

6. Motor Performance Monitoring — Industrial IoT (1st place, Tec Expo Ingenierías)

   github.com/DArmandoSalinas/Motor-performance-prediction

   Python, Streamlit, signal processing, serial telemetry, statistical thresholding

   • •Won first place at Tec Expo Ingenierías by re-engineering an existing plant maintenance procedure into a live monitoring system, profiling a Sumitomo three-phase motor across five speed baselines and a Haas Mini Mill from combined trajectories.
   • •Scored machine health 0–100 from threaded serial vibration and temperature telemetry with machine-specific adaptive thresholds, keeping the design fully rule-based so every alarm traces to the statistic that fired it; recognized by Rockwell Automation for the integration work.

Education

1. MSc Artificial Intelligence — Distinction

   2024 — 2025

   University of Essex, United Kingdom

   • •Thesis: Analysis of Heart Rate Variability using PPG signals to detect and predict emotions through Machine Learning. Modules: Machine Learning; Intelligent Systems and Robotics; Data Science and Decision Making; Neural Networks; Natural Language Engineering.

2. B.Sc. Mechatronics Engineering

   2021 — 2025

   Tecnológico de Monterrey, Mexico

   • •Coursework: control systems, industrial automation, robot design, manufacturing.