@issaiass (Rangel Alvarado) -

Electromechanical Engineer

's skills

's Work Experience

Panamerican Semiconductors Inc

Role:

From 2006-02-01 to 2008-02-08

Develop embedded systems applications using microcontrollers and FPGAs. Systems based on SoPC Builder usin NIOS II and Cyclone V. Embedded Systems using Motorola HC08 MCU

ENSA

Role:

From 2008-02-08 to 2011-08-01

|| Develop, Test and Commission the whole SCADA system of the company from field devices like protective relays. Mixing between media converters, MUX, DEMUX, switches, routers, programming PLCs and PACs as concentrators. Develop the SCADA Database (fill), dispatcher windows, OMS reports and maintainance.

ENSA

Role:

From 2011-08-01 to 2013-10-01

Responsible of the Maintainace and Continuous Development of the SCADA System A group of 2 database administrators and 3 field service engineers Develop new projects for substations Continue to launch and develop new projects on the field

EATON Electrical

Role:

From 2013-10-01 to 2019-12-30

|| Responsible for the Panama Canal 3rd Locks Projec, Atlantic Site || || Responsible for the Tocumen Intl. Airport Expansion and SCADA Bringup || Field Service for VFDs, lighting systems, automation and control, protective relaying, Automatic Transfer Switches, Soft Starters, breakers and PLCs

Sprout AI Solutions

Role:

From 2020-02-08 to 2022-06-06

Implement the new era of IIoT products for the company that is based on a vertical cultivation system PLC based IIoT using Azure or AWS to send and control over this platform. Sense field equipment and vertical cultivation control for the habitat Develop the new product and internal client requirements

DATA ACQUISITION SYSTEMS, S.A. (DAQ)

Role:

From 2008-01-01 to current date

Started to develop embedded systems products, focused on computer vision, robotics and AI for improve life and learn about computer vision to focus on the cliend requirements

Banco Lafise

Role:

From 2022-06-07 to current date

- Preprocess data and prepare a clean dataset for ingest in ML applications on the cloud - Analyze data to gather insights and adjust later to do feature engineering - Visualize data to validate insights of the data - Using AWS Machine Learning Framework to train complex models - Prepare the pipeline for future use and online learning - Make A/B Testing - Put the model in production and monitor performance

Main Links