Kimia Gholizadeh
Artificial Intelligence Engineer @Pong Game Studios
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WORK HISTORY
Artificial Intelligence Engineer @Pong Game Studios
Woodbridge, ON, CA
As the only AI Engineer in the company, I use Python to design, build, deploy, and maintain all internal AI systems used by Development, Systems, QA, Tech Support, Sales, and Product. I gather requirements across teams, lead R&D and architecture design, define project roadmaps, and own full Azure/Docker/CI/CD deployment with authentication and monitoring. I also work with modern agentic frameworks such as LangChain, LangGraph, and LangSmith, and apply best practices for RAG memory management and agent-performance evaluation.• Agentic RAG Assistant: Built in Python using OpenAI LLMs, FAISS, rerankers, and LangChain/LangGraph-style orchestration patterns. Ingests Confluence + internal documents and produces role-specific, source-linked answers with grounding, memory, and retrieval evaluation.• Automated Visual QA System: Developed a Python-based multi-modal QA agent using YOLOv12, Tesseract OCR, Selenium, and PyGUI to automate gameplay flows, validate transitions and calculations, analyze help pages with LLM-based grammar/terminology checks, and generate Jira-style reports.• LLM-Powered Log Analyzer: Engineered a Python log-analysis agent using fine-tuned LLM reasoning and an internal error catalog to detect SSL/HTTP/hardware issues, evaluate reasoning reliability, and generate developer-style root-cause reports. Includes a support chatbot for operational queries.
EDUCATION
University of Windsor
Master of Applied Science, Electrical Engineering (Specialization in Artificial Intelligence)
Mazandaran University of Science and Technology
Bachelor of Engineering - BE, Computer Software Engineering
ABOUT KIMIA GHOLIZADEH
I am the sole AI Engineer at Pong Game Studios, reporting directly to the IT Manager and owning AI projects end-to-end, from requirement gathering to architecture, implementation, deployment, and weekly progress reporting.I work across system, backend, frontend developers, QA, tech support, and sales teams to define problems, design technical solutions, and deliver production-ready AI systems that automate workflows, improve decision-making, and reduce operational effort.My work spans LLMs, RAG systems, agentic workflows, computer vision, and log-based diagnostics, with strong experience in:• Python (advanced proficiency)• OpenAI LLMs, FAISS, RAG pipelines, rerankers• Agentic AI design for multi-role use cases (LangChain, LangGraph)• Evaluation concepts inspired by LangSmith (grounding checks, reasoning validation, retrieval scoring)• YOLOv12, OCR, Selenium, and PyGUI for automated visual QA• Error-diagnosis agents using fine-tuned LLMs and Datalog-style reasoning• Azure cloud, Docker, CI/CD, authentication & access controlI specialize in building production-grade AI systems that:• Retrieve documentation from Confluence + local sources• Generate role-aware answers with source citations• Automate QA workflows for slot games• Detect errors, analyze logs, and generate developer-ready reports• Provide tech support with real-time device insights and operational queriesI hold a Master’s in Artificial Intelligence from the University of Windsor, where my research focused on deep learning–based Multi-Object Tracking (MOT) with an emphasis on memory optimization.I thrive in environments where I can take a project from 0 → 100, owning requirements, R&D, architecture, roadmapping, deployment, and iteration
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