POWER SYSTEMS × APPLIED AI

Power systems.
Applied AI.

I’m Amir Exir, a power systems and applied AI engineer. I combine electrical engineering experience with AI development to build knowledge agents, machine learning applications, analytics dashboards, and engineering automation tools.

Licensed P.E.NERC CertifiedUT Austin AI graduate student
Amir Exir in protective equipment at an electrical substation
IN THE FIELD
Engineering experience.Applied AI. Practical automation.
Austin, Texas Energy & utilitiesSCROLL TO EXPLORE
01
Transmission planningModels, studies & interconnection
02
Grid operationsReliability & real-time engineering
03
Engineering automationPython, PSS/E & TARA PowerGEM
04
Applied AIKnowledge agents, forecasting & ML

SELECTED WORK / 01

Complex systems.
Useful solutions.

Explore engineering tools, AI knowledge agents, predictive models, and interactive dashboards—from power system studies to document research and market analytics.

Professional Summary

An engineer’s perspective.
A builder’s mindset.

Portrait of Amir Exir
Amir Exir, P.E.Power systems + applied AI

I am a Professional Engineer and NERC Certified System Operator with a background in electrical and computer engineering, experience at ERCOT, LCRA, and PEC, and ongoing graduate study in AI at UT Austin. My work combines transmission planning, grid operations, and energy management system (EMS) applications with software and AI development.

I build Python applications, retrieval-augmented knowledge agents, and machine learning models for technical research, forecasting, classification, and automation. Power systems provide the foundation for much of this work; additional projects explore language-model fine-tuning, market analytics, and automated reporting.

  • PSS/E and TARA PowerGEM workflow automation for planning studies and reporting.
  • Technical document search across ERCOT protocols, guides, working group manuals, resource integration procedures, and revision requests.
  • Machine learning applications for load forecasting, fault classification, power grid alarms, and market analytics.

Credentials

Engineering foundations.
Continued learning in AI.

Professional licensure and system operator certification, alongside graduate education in electrical and computer engineering, ongoing AI study, and coursework in machine learning and cloud computing.

NCEES PE Exam badge

Professional Engineer

Texas P.E. license with power systems and transmission planning focus.

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University of Texas at Austin logo

UT Austin M.S. in AI In progress

Graduate AI coursework in machine learning, optimization, deep learning, ethics, and applied systems.

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Lamar University logo

M.Eng. Electrical & Computer Engineering

Lamar University, 2020. Master of Engineering in Electrical and Computer Engineering.

AWS Cloud Practitioner badge

AWS Cloud Practitioner

Certification in AWS cloud fundamentals.

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Explore course badges & certificates
Automating PSS/E Using Python Training in Python scripting for PSS/E study automation. Download Certificate
IBM AI Certificate Applied AI coursework covering machine learning foundations, Python, and data-driven development. Download Certificate
Machine Learning Certificate Machine learning coursework supporting the forecasting and classification projects shown here. Download Certificate
Deep Learning Badge UT Austin coursework in deep learning and neural networks. Download Badge
Ethics in AI Badge Coursework in AI ethics and responsible use of AI systems. Download Badge

Experience

Transmission planning
and grid operations.

Planning, operations, advanced applications, and training experience across PEC, LCRA, and ERCOT.

Transmission Planning Engineer 4

PEC | Jan 2026 - Present | Austin, TX

Conduct transmission planning studies, evaluate projects, and analyze system reliability using power system models.

Transmission Planning / EMS Advanced Applications Engineer

LCRA | Aug 2022 - Jan 2026 | Austin, TX

Worked across planning studies, EMS advanced applications, GE EMS SCADA/TSM/DTS workflows, modeling support, and engineering automation.

Resource Integration / Operations Training / Real-Time Engineering

ERCOT | Oct 2019 - Aug 2022 | Austin, TX

Supported ERCOT resource integration, operations training, real-time engineering, model requirements, and market/reliability procedures.

Associate / Substitute Teacher

CFISD / HISD | 2018 - 2022 | Houston, TX

Classroom teaching and instructional support, with an emphasis on explaining technical concepts to students with varied backgrounds.

Flagship Tool

AELab.
Built for the study workflow.

AELab brings PSS/E and TARA PowerGEM study tasks into a Python desktop application. It supports contingency analysis, dynamic simulation, model validation, results visualization, IDV generation, and transmission project reporting.

Study capabilities

  • Run batch contingency analysis and report thermal overloads, voltage violations, and voltage deviations.
  • Process dynamic simulation outputs, run events, summarize channels, and generate plots.
  • Compare PSS/E cases and power flow, dynamic, and contingency results to support model validation.
  • Visualize TARA PowerGEM TRLIM and contingency results for transmission upgrade and hosting-capacity review.
  • Generate IDV files from line impedance and rating data for model updates.
Python PSS/E TARA PowerGEM GUI automation Planning studies

Interactive Grid Map

Explore transmission
and generation.

Explore transmission lines, substations, and power plants on an interactive map of U.S. grid regions and Canada. Search for facilities, filter infrastructure by region, and review available asset and source information.

Data & AI Solutions

Technical knowledge.
Easier to navigate.

Retrieval-augmented AI assistants for technical document research. Search ERCOT procedures and PSS/E API references, retrieve relevant passages, and review answers alongside source citations.

ERCOT DWG & SSWG Knowledge Agent

Answers questions from ERCOT Dynamic and Steady-State Working Group manuals, including flat-start case development, model validation, and compliance support.

ERCOT Planning Guide Knowledge Agent

Searches ERCOT planning procedures and requirements for faster navigation of standards, revisions, and study expectations.

ERCOT Resource Integration Knowledge Agent

Supports interconnection process questions around data submittals, timelines, responsibilities, and public documentation requirements.

ERCOT Nodal Protocols Knowledge Agent

Knowledge agent for ERCOT protocol research across market rules, operating procedures, settlement topics, and technical requirements.

PSS/E API Knowledge Agent

Find PSS/E API references and scripting examples for power flow, contingency analysis, and dynamic simulation.

PSS/E Multi-Agent Automation System

Develop PSS/E automation scripts with coordinated AI assistants for task planning, API lookup, code generation, execution, and error handling.

Professional-use note: AI-generated answers require engineering review. Verify citations, document status, and effective dates against official sources before using an answer in a study, operating procedure, or compliance decision.

Applied ML

Machine learning.
Practical applications.

Explore forecasting, classification, graph neural networks, and language-model fine-tuning. Applications include power grid analysis, energy demand forecasting, medical question answering, and interview preparation.

Power Grid GNN Predictor

Graph neural network system for simulated power grid scenarios that predicts voltage violations and thermal overloads across buses and transmission lines using GCN, GAT, GIN, and Graph Transformer architectures.

PyTorch Geometric Pandapower Grid topology Classification
Fault classifier confusion matrix

Power Fault Classifier

Explore fault type classification from current and voltage measurements in a Scikit-learn and Streamlit application for protection training and diagnostic research.

Hourly Load Forecast App

Explore hourly load forecasts for AEP/PJM using historical demand data and interactive model analysis.

TinyLlama Medical Q&A Fine-Tuning

LoRA fine-tuning project using TinyLlama-1.1B-Chat and MedQuAD, with ROUGE-based evaluation and resource-aware training techniques.

AI Interview Assistant

Practice behavioral and technical interviews with an assistant that draws on résumé content and STAR examples.

Automation Case Study

Market data analysis
and paper trading.

An experimental stock and cryptocurrency application that compares forecasting models, summarizes market data, and supports Alpaca paper trading. Reports can be requested through Telegram or delivered on a schedule using n8n and calendar integrations. This demonstration does not provide financial advice.

Analysis and reporting

  • Collect market data and evaluate trading workflows through Alpaca paper trading.
  • Compare, validate, and rank forecasting models before publishing signals.
  • Request individual analyses or schedule reports through Telegram.
  • Save report versions and deliver summaries through Telegram and Google Calendar.

Personal

Beyond the desk.
Around Austin.

I enjoy biking and paddleboarding around downtown Austin, and I make time for the live music culture that makes Austin, Texas, feel like home.

Contact

Good work starts
with a conversation.

I welcome conversations about power systems, AI engineering, and software development. Contact me to discuss engineering automation, knowledge agents, machine learning, analytics, or solutions architecture for energy and other technical applications.