Luis Gonzalez

Luis Gonzalez

About

Detail

AI & Data Specialist | GenAI, Computer Vision & MLOps
Lima Province, Peru

Contact Luis regarding: 
work
Full-time jobs

Timeline


work
Job
school
Education
folder
Project

Résumé


Jobs verified_user 0% verified
  • National University of Engineering
    Thesis Researcher in Chemical Engineering/Artificial Intelligence
    National University of Engineering
    Aug 2024 - Oct 2024 (3 months)
  • GRUPO PALMAS
    Semisenior Refined Products Analyst
    GRUPO PALMAS
    Jun 2023 - Apr 2024 (11 months)
  • Osinergmin
    Natural Gas Engineer
    Osinergmin
    Mar 2023 - Jun 2023 (4 months)
  • The Princeton Review
    Intermediate Tutor
    The Princeton Review
    Mar 2022 - Current (4 years 6 months)
  • BSH HOME APPLIANCES GROUP
    Process Engineer
    BSH HOME APPLIANCES GROUP
    Mar 2022 - Mar 2023 (1 year 1 month)
  • MAPRIAL SAC
    Chemical Engineer
    MAPRIAL SAC
    Oct 2021 - Dec 2021 (3 months)
  • Organismo de Evaluación y Fiscalización Ambiental OEFA
    Curso de Extensión Universitaria en Fiscalización Ambiental
    Organismo de Evaluación y Fiscalización Ambiental OEFA
    Jan 2021 - Feb 2021 (2 months)
Education verified_user 0% verified
  • National University of Engineering
    Master's degree
    National University of Engineering
    Jan 2025 - Jun 2026 (1 year 6 months)
  • National University of Engineering
    Titulado en Ingenieria Química
    National University of Engineering
    Jan 2024 - Dec 2025 (2 years)
  • Universidad Nacional de Colombia
    Bachelor of Science - BS
    Universidad Nacional de Colombia
    Jan 2021 - Dec 2021 (1 year)
  • National University of Engineering
    Bachelor of Science - BS
    National University of Engineering
    Jan 2017 - Dec 2021 (5 years)
  • Universidad Nacional Mayor de San Marcos
    Bachelor of Science - BS
    Universidad Nacional Mayor de San Marcos
    Jan 2016 - Dec 2016 (1 year)
  • National University of San Marcos
    Bachelor of Science - BS
    National University of San Marcos
    Jan 2016
  • National University of Engineering
    National University of Engineering
    National University of Engineering
Projects (professional or personal) verified_user 0% verified
  • P
    PhenoBerry
    Dec 2025
    Developed PhenoBerry, a computer vision-based system for phenotyping and yield estimation in berry crops. The solution leverages deep learning models to detect, count, and classify berries from image data, enabling automated monitoring of crop development and ripeness stages. The pipeline is designed to support precision agriculture use cases, particularly forecasting harvest readiness and optimizing field operations. Key technical components: • Implemented object detection and/or instance segmentation models for berry localization • Designed image processing pipeline for feature extraction and phenological analysis • Built data preprocessing workflows for handling field-acquired image variability (lighting, occlusion, scale) • Developed in
  • M
    Multimodal AI System for Industrial Visual Inspection
    Nov 2025 - Dec 2025 (2 months)
    Developed a multimodal AI system for automated visual inspection in industrial environments, combining computer vision and language models to detect, explain, and report defects in real time. The system integrates a vision backbone (CNN/ViT) with a multimodal model inspired by architectures like CLIP, enabling semantic understanding of visual defects. A key component is the use of LLMs to: Generate human-readable defect reports Explain model predictions (XAI layer) Assist operators with corrective recommendations The solution processes images from production lines and outputs: Defect classification Localization (bounding boxes / segmentation) Natural language explanations of issues Deployed as an API-based microservice integrated into manuf
  • A
    Attention-Based Visual Analysis for Blueberry Phenotyping (Pre-Production Research)
    Jul 2025 - Sep 2025 (3 months)
    Conducted applied research on transformer-based computer vision models to understand how attention mechanisms capture spatial patterns in high-density agricultural imagery. Explored Vision Transformers (ViT) and Swin Transformers, analyzing attention maps to identify how models detect fruit clusters, occlusions, and maturity indicators in blueberry crops. Benchmarked transformer architectures against CNN-based models, identifying improvements in global context understanding and robustness in complex visual scenarios. Built reproducible experimentation pipelines with cloud-based processing and experiment tracking, enabling systematic evaluation of model performance. This research served as a foundational step for the development of productio
  • A
    AWS Stock AI Agent
    Jul 2025
    Developed an AI-powered stock analysis agent using AWS, designed to automate financial insights and support decision-making. The system integrates LLM capabilities with cloud-native architecture to process market data, generate insights, and respond dynamically to user queries. Key highlights: • Built serverless architecture using AWS (Lambda, API Gateway, S3) • Integrated AI/LLM components for intelligent analysis • Designed scalable and modular pipeline for financial data processing Tech stack: Python, AWS, LLMs, APIs
  • D
    Deep Learning for Demand Sensing & Supply Chain Optimization
    May 2022 - Sep 2022 (5 months)
    Developed an end-to-end deep learning system for demand sensing and short-term forecasting in retail supply chains, enabling near real-time inventory optimization and reduction of stockouts. The research explores advanced sequence modeling architectures, benchmarking LSTM-based models against transformer-based approaches such as Temporal Fusion Transformer, focusing on capturing complex demand patterns influenced by promotions, seasonality, and external signals. The system integrates: Historical sales data Promotions and pricing signals External features (holidays, weather proxies) Key innovation: Dynamic feature importance (interpretability layer) Multi-horizon forecasting (daily / weekly) Real-time demand sensing (short-term adjustments)