Data Architecture & ML Engineer

Manuel Caipo
Manuel Caipo

Machine Learning Engineer

University Ulm
Hochschule Furtwangen

About Me

I began my journey in Peru, where I graduated top of my class in Mechanical Engineering from the Universidad Nacional de San Agustín de Arequipa (UNSA).
My academic performance earned me the prestigious Beca Presidente del Perú, a national full scholarship awarded to the country’s top students.

After graduation, I was selected as one of 100 finalists out of 16,000 applicants to join Freeport-McMoRan, where I developed Machine Learning solutions for predictive maintenance (Remaining Useful Life) across global mining operations, combining: data science, reliability engineering, and real-world industrial systems.

Currently, at Bosch Rexroth, I design and orchestrate Docker-based ETL pipelines, real-time monitoring systems, and machine/deep learning models for predictive maintenance of hydraulic systems.
My work bridges industrial automation and AI-driven data architectures, connecting physical assets with scalable, intelligent software systems.

Focus Areas

  • AI Architectures & Time-Series Intelligence
    Development of advanced ML/DL architectures including: LSTM networks with attention CNN-based temporal filters and survival models for Remaining Useful Life (RUL) prediction, anomaly detection, and adaptive retraining in high-frequency industrial data environments.
    Expertise in explainable AI using SHAP, PDP, and interpretability frameworks for model transparency and reliability.

  • Scalable Data & ETL Orchestration
    Design of modular ETL pipelines and multi-container architectures using Dagster, Apache Airflow, and Docker.
    Implementation of real-time dataflows integrating SQL/NoSQL systems (PostgreSQL, Snowflake, InfluxDB) and streaming technologies (Kafka, Solace, MQTT) for industrial telemetry.

  • Industrial Connectivity & Cyber-Physical Systems
    Integration of OPC UA, MQTT, and CtrlX Core communication frameworks for robust data acquisition across hydraulic, robotic, and automation systems.
    Bridging control theory, system identification, and AI-based condition monitoring to enable self-diagnostic industrial assets.

  • Cloud, DevOps & Scalable Deployment
    End-to-end deployment of ML pipelines using Azure Machine Learning and Databricks, with full CI/CD integration and containerized workflows.
    Strong focus on reproducibility, versioned data pipelines, and edge-cloud synchronization for Industrial AI.

I aim to pursue a Ph.D. in Robotics, Control Systems, or Industrial AI, developing impactful technologies that bridge data and physical systems.

Analytics

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Interests
  • Smart Systems for Industrial Engineering and Robotics
  • Technical Software Architecture & Data Automation for Industry 4.0
  • Fusion of Data Science with Physical Modeling
Education

  • M.Sc. Computational Science and Engineering

    University Ulm

  • M.Sc. in Advanced Precision Engineering

    Hochschule Furtwangen

  • Diplom in Machine and deep Learning

    Universidad catolica san pablo

  • B.Sc. in Mechanical Engineering

    Universidad Nacional de San Agustin de Arequipa

Professional Journey
2024–Present | Data Scientist Werkstudent | Masterand – Bosch Rexroth (Ulm, Germany)
  • Industrial ETL Pipeline: Designed and implemented a complete ETL process to transmit CtrlX sensor data to a relational database via Solace messaging, fully containerized in Docker
  • Data Processing Optimization: Developed and optimized stored procedures for restructuring raw sensor streams into machine-learning-ready formats
  • RUL Prediction & Orchestration: Built predictive models for Remaining Useful Life (RUL) of hydraulic systems using Hidden Markov Models, clustering, and XGBoost — all orchestrated via Dagster
2021–2023 | Data Science – Freeport-McMoRan (Global Mining Operations)
Presidential Award – July 2022
Honored with the President’s Award and Innova 2022 (1st Place Digital Transformation) for developing predictive wear and failure models that improved plant availability by +1.5% (~10 M USD/year impact).
  • Global ML Deployment: Scaled Azure ML pipelines delivering daily wear predictions for 200+ heavy assets (crushers, HPGRs, mills).
  • Data Infrastructure Optimization: Reduced SQL data preparation time from 8 h → 22 min through stored procedure re-engineering and pipeline parallelization.
  • Adaptive Learning Systems: Implemented continuous retraining and model monitoring over 5 years of multivariate operational data.
  • Decision Intelligence: Built Power BI environments visualizing KPIs for maintenance optimization and operational reliability.
2015–2021 | Engineering Foundations (Peru)
  • IMCO Servicios (2018–2021): Concurrent engineering roles during undergraduate studies:
    • Conducted finite element (FEM) and CFD simulations for mechanical structures and mining components (SAP2000, Ansys, Autodesk CFD).
  • Academic Excellence: Graduated top 1 % in Mechanical Engineering from UNSA (Peru), awarded the national Beca Presidente de la República.
Academic Milestones
Featured Publications
Dynamic Modeling and Control of a 5-DoF SCARA Robot using MATLAB and ROS2
Künstliche Intelligenz in der Präzisionsmesstechnik
Design of a 350 TPH Copper Concentrate Feeding Conveyor Belt with Automated Hydraulic Tensioning System and Receiving Hopper

Experience

  1. Data Scientist – Werkstudent

    Bosch Rexroth
    • Designed and implemented an ETL pipeline in Docker for processing and storing industrial data in a data lake and relational database.
    • Integrated Solace as a message broker to optimize industrial data flow.
    • Developed a monitoring system for hydraulic systems to detect anomalies and analyze operating cycles.

  2. Junior Data Science Analyst 2

    Freeport-McMoRan – Cerro Verde
    • Scaled predictive wear models across global mining sites using Azure ML Jobs for automated predictions.
    • Optimized data preprocessing using SQL stored procedures for efficient ML training.
    • Deployed retraining automation processes to continuously update models with historical machine data.

  3. Junior Data Science Analyst 1

    Freeport-McMoRan – Cerro Verde
    • Deployed ML models for daily wear prediction of primary crushers and cyclone pumps using Azure ML.
    • Automated reporting systems to compare current machine states with historical performance data.

  4. Junior Data Analyst 1 – Reliability Engineering

    Freeport-McMoRan – Cerro Verde
    • Developed machine anomaly strategies based on historical failure analysis.
    • Created Power BI dashboards and reports for monitoring and decision-making.
    • Optimized SQL queries for faster data processing in maintenance analytics.

  5. Trainee Data Analyst – Reliability Engineering

    Freeport-McMoRan – Cerro Verde
    • Performed long-term analysis of equipment availability and operational patterns.
    • Focused on primary crushers, conveyors, cyclone pumps, ball mills, and HPGR systems.

  6. Junior Engineer – Technical and Development

    IMCO Servicios S.A.C
    • Created structural calculation reports using simulation software like SAP2000, Inventor, Ansys Structural - Fluent and AutoCAD 3D.

  7. Intern – Reliability Engineering

    Freeport-McMoRan – Cerro Verde
    • Optimized production processes using multiphysics CFD simulations with tools such as Ansys Structural, Fluent, Tekla, and Ametank.

Education

  1. M.Sc. Computational Science and Engineering

    University Ulm
    Master of science.

  2. M.Sc. in Advanced Precision Engineering

    Hochschule Furtwangen
    Master of science.

  3. Diplom in Machine and deep Learning

    Universidad catolica san pablo
    GPA: 19.6/20.0

  4. B.Sc. in Mechanical Engineering

    Universidad Nacional de San Agustin de Arequipa

    GPA: 15.23/20.0

    National Scholarship “Beca Presidente del Peru” Winner, ranking the first in the department for five consecutive years.

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