Professional Engineer
Texas P.E. license with power systems and transmission planning focus.
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Licensed P.E. | NERC Certified | UT Austin AI graduate student
Power systems engineer building AI tools for grid planning, operations, and automation.
I combine utility-scale engineering experience with applied AI to build practical assistants, analytics dashboards, and PSS/E automation tools for high-stakes power system workflows.
Transmission planning
Modeling, interconnection, contingency, and project analysis experience.
Grid operations
ERCOT real-time operations, training, and reliability coordination background.
Automation systems
PSS/E, TARA PowerGEM, Python, Streamlit, n8n, and AI-assisted workflows.
Applied AI
RAG assistants, GNN grid models, forecasting, and fine-tuning projects.
Professional Summary
Engineering judgment with hands-on AI execution.
Amir Exir is a Professional Engineer and NERC Certified System Operator with power system experience across ERCOT, LCRA, and PEC. His work sits at the intersection of transmission planning, grid operations, EMS advanced applications, interconnection support, and Python-based engineering automation.
His portfolio is built around practical systems: AI assistants trained on public ERCOT and PSS/E materials, grid dashboards, contingency analysis tooling, GNN-based alarm prediction, and workflow automation for engineers who need answers quickly and defensibly.
- PSS/E and TARA PowerGEM workflow automation for planning studies and reporting.
- ERCOT documentation assistants for nodal protocols, planning guides, DWG/SSWG manuals, and resource integration.
- Machine learning applications for load forecasting, fault classification, power grid alarms, and market analytics.
Credentials
Licenses, education, and certificates.
Focused credentials that support both sides of the portfolio: power engineering reliability work and modern AI systems development.
NERC Certified System Operator
Reliability Coordinator certification and real-time grid operations background.
Discuss grid operations experience
UT Austin M.S. in AI
Graduate AI coursework in machine learning, optimization, deep learning, ethics, and applied systems.
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M.Eng. Electrical & Computer Engineering
Lamar University, 2020. Graduate engineering foundation for power, computation, and automation work.
AWS Cloud Practitioner
Cloud fundamentals for deployable AI tools, dashboards, and data workflows.
View credentialDownloads
Resume, PSS/E automation certificate, IBM AI certificate, machine learning certificate, and AI course badges.
Experience
Power systems work across planning, operations, and training.
The portfolio reflects work shaped by real utility and market operations environments, not generic AI demos.
Transmission Planning Engineer 4
Transmission planning role focused on model-driven studies, project evaluation, reliability analysis, and engineering decision support.
Transmission Planning / EMS Advanced Applications
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
Supported ERCOT resource integration, operations training, real-time engineering, model requirements, and market/reliability procedures.
Associate / Substitute Teacher
Teaching experience that strengthened communication, training delivery, and technical explanation for mixed audiences.
Flagship Tool
AELab power system analysis suite.
AELab is a Python GUI suite built to reduce repetitive study work around PSS/E and TARA PowerGEM: contingency analysis, dynamic simulation, model validation, results visualization, IDV generation, and transmission project reporting.
What it demonstrates
- Batch contingency analysis with thermal, voltage, and voltage deviation reporting.
- Dynamic simulation parsing, event runner support, channel summaries, and automated plotting.
- PSS/E model validation by comparing cases across power flow, dynamic, and contingency outputs.
- TARA PowerGEM TRLIM and contingency result visualization for upgrade and hosting-capacity review.
- IDV generation from impedance and rating data for line design and model update automation.
Python
PSS/E
TARA PowerGEM
GUI automation
Planning studies
AI Assistants
RAG tools for ERCOT and PSS/E knowledge work.
These assistants convert dense public technical documentation into searchable, conversational tools for engineers, developers, planners, and analysts. Outputs still require professional validation before operational or compliance use.
ERCOT All-in-One AI Assistant
Assistant over ERCOT nodal protocols, planning guides, interconnection handbook content, and DWG/SSWG manuals. Built for questions about submissions, modeling requirements, dynamic validation, and market rule navigation.
ERCOT
RAG
Streamlit
Engineering QA
ERCOT DWG & SSWG Assistant
Answers questions from ERCOT Dynamic and Steady-State Working Group manuals, including flat-start case development, model validation, and compliance support.
ERCOT Planning Guide Assistant
Searches ERCOT planning procedures and requirements for faster navigation of standards, revisions, and study expectations.
ERCOT Resource Integration Assistant
Supports interconnection process questions around data submittals, timelines, responsibilities, and public documentation requirements.
ERCOT Nodal Protocols Assistant
Assistant for ERCOT protocol research across market rules, operating procedures, settlement topics, and technical requirements.
PSS/E API Assistant
Copilot-style assistant over curated PSS/E API materials and examples for contingency analysis, dynamics, load flow, and scripting workflows.
PSS/E Multi-Agent Automation Bot
Multi-agent scripting workflow combining task planning, retrieval, code generation, execution, and retry logic for more complex PSS/E automation tasks.
Professional-use note: These assistants use public documentation and semantic search to accelerate research. AI output can be incomplete or inaccurate, so users must validate against official documents before applying results in operational, regulatory, financial, or compliance settings.
Applied ML
Machine learning projects with grid and engineering context.
These projects demonstrate modeling breadth: graph neural networks, fault classification, load forecasting, healthcare fine-tuning, and workflow assistants.
ERCOT Load Forecast Dashboard
Primary grid analytics project for ERCOT load forecasting, renewable generation, real-time market pricing, outage views, news monitoring, regulatory updates, and Telegram publishing.
ERCOT API
Load forecasting
Grid analytics
n8n
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
Power Fault Classifier
Interactive classifier for fault type prediction using current and voltage measurements. Built with Scikit-learn and Streamlit for protection training and diagnostics exploration.
Hourly Load Forecast App
Forecasting app for hourly energy consumption in AEP/PJM regions using historical load data and interactive model exploration.
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
Interview preparation assistant trained on resume content and STAR stories to simulate behavioral and technical interview practice.
Automation Case Study
Stock Market & Crypto AI Agent.
A public demo of multi-source automation: market data, model comparison, AI summaries, Telegram-triggered runs, n8n workflows, calendar delivery, and Alpaca paper trading. It is a software and ML project, not financial advice.
System scope
- Live market data ingestion with paper trading integration through Alpaca.
- Model comparison, validation, and ranking before publishing signals.
- Telegram natural-language command routing for one-off and scheduled runs.
- Automated summaries, file versioning, and delivery to Telegram and Google Calendar.
Personal
A little context beyond the engineering work.
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
Open to power systems, AI automation, and grid analytics conversations.
Best fit topics include transmission planning automation, ERCOT process tooling, EMS/operations support tools, RAG assistants for technical documentation, and applied ML for power system analysis.