Hi, I'm Pranshu Ghori
AI Engineer · LLM Agents · RAG Systems
Building intelligent systems at the intersection of data, language, and autonomous AI.
About Me
AI Engineer building production-grade agents and intelligent systems.
I'm pursuing concurrent degrees in B.S. Artificial Intelligence (STEM) and B.S. Business Data Analytics (STEM) at Arizona State University, maintaining a 4.0 GPA. My focus sits at the intersection of language models, autonomous agents, and real-world data systems.
I specialize in LLM engineering, agentic orchestration with LangGraph, and RAG pipeline design. From building multimodal compliance auditing systems on Azure — using Azure Video Indexer, Azure AI Search, and GPT-4o — to production RAG pipelines over FAISS and Pinecone, I engineer AI systems that reason, retrieve, and act with full observability via LangSmith and Azure Application Insights.
I'm particularly drawn to autonomous AI agents, domain-specific tooling, and applied generative AI — systems where language models move beyond chat and become reliable, observable components in production workflows. My ML engineering background in Scikit-learn and MLOps grounds the AI work in solid engineering fundamentals.
LLM Engineering
Prompt engineering, API integration, and chaining LLMs into reliable, production-grade pipelines.
Agentic Systems
Multi-agent orchestration with LangGraph — planning, tool use, reflection, and conditional routing.
RAG & Retrieval
Vector search, embeddings, and knowledge retrieval using FAISS, Pinecone, and Azure AI Search.
Skills & Technologies
The stack I use to design, build, and deploy intelligent AI systems.
Agentic AI & LLM Engineering
Machine Learning & Deep Learning
Software Engineering
Data & Visualization
Experience
My professional journey in data analytics and web technologies.
Data Analytics Assistant
- Analyzed network hardware inventory data in Python, Pandas, and SQL across 1,000+ assets — identifying discrepancies, null fields, and duplicate records to improve data quality for audit cycles.
- Built automated ETL pipelines to clean, reconcile, and visualize inventory datasets using Python and Power BI — eliminating manual reconciliation steps and delivering recurring dashboards to infrastructure leadership.
Web Assistant
- Maintained 20+ university web pages using HTML and CSS; performed pre-publication QA validation catching layout breaks, dead links, and responsive failures before production.
ML Engineer Intern
- Assisted the ML engineering team building end-to-end production ML pipelines — contributing to data preprocessing, feature engineering, and model training workflows using Python and Scikit-learn.
- Built and tested classification and regression models using Scikit-learn, supporting model selection, hyperparameter tuning, and performance benchmarking across multiple production use cases.
- Supported MLOps workflows by contributing to model deployment and monitoring tasks — gaining hands-on experience with production-grade ML lifecycle management and pipeline integration.
Featured Projects
AI agents, RAG systems, and data pipelines — built end-to-end.
Video Compliance QA Pipeline
- Built a production-grade video compliance auditing system orchestrated by LangGraph — ingesting multimodal content via Azure Video Indexer (transcripts + OCR) and detecting regulatory violations using RAG powered by Azure AI Search and Azure OpenAI embeddings.
- Engineered the core reasoning engine using GPT-4o to deterministically synthesize compliance rules against extracted video content, generating structured JSON reports.
- Integrated LangSmith for LLM tracing and Azure Application Insights for production-grade telemetry and full-stack observability.
- Designed end-to-end modular architecture with clean separation across ingestion, retrieval, reasoning, and reporting stages — deterministic outputs with deep observability at every layer.
Corporate Brochure Generator
- End-to-end pipeline that scrapes any corporate website and generates a polished company brochure
- Fetches all links from a homepage, then uses Grok to filter only relevant pages (About, Products, Careers, etc.)
- Scrapes each selected page and passes consolidated content to Grok for brochure generation
- Outputs formatted Markdown ready for publishing or export
- Modular structure: scraper.py handles all web utilities, brochure.ipynb orchestrates the full pipeline
DocumentLoader — Production RAG Pipeline
- Built a production-ready RAG pipeline supporting natural language Q&A over custom documents with streaming responses, source citations, and multi-turn memory — reducing retrieval latency via FAISS nearest-neighbor search and dynamic document ingestion at runtime.
- Orchestrated the full retrieval-to-reasoning loop using LangChain and LangGraph — context retrieval, prompt construction, LLM chaining, and fallback handling.
- Architected for full provider portability (FAISS → Pinecone, OpenAI → Anthropic) without re-architecting the pipeline.
California Housing Price Prediction
- Built an end-to-end regression ML pipeline
- Used stratified train/test split, preprocessing with ColumnTransformer, and unified Pipeline
- Engineered predictive features like log transforms, ratio metrics, geo-cluster similarity
- Evaluated using cross-validation and RMSE
- Saved deployable artifact with joblib
U.S. Flight Delay & Cancellation Analysis
- Analyzed 3 million U.S. flight records
- Identified delay and cancellation patterns by airline, route, season, and operational cause
- Engineered time-based features such as hour, weekday, and season
- Found late aircraft and carrier operations were major contributors to delay minutes
- Produced visual reports with actionable insights
Education
Arizona State University
Tempe, AZ
B.S. Business Data Analytics (STEM-Designated)
B.S. Artificial Intelligence (STEM-Designated)
Concurrent Degrees
Aug 2024 – Dec 2027
Get In Touch
Currently seeking Fall 2025 and Summer 2026 data analytics and ML internships.
Contact Information
Resume
Download my full resume to see a complete list of my skills, experiences, and academic achievements.
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