You will get AI Resume Parsing Pipeline — LLM Extraction, Company Normalization & Skills


Project details
A production-architecture resume intelligence system that solves the core problem most HR tech platforms struggle with — accurate structured extraction from unstructured, inconsistent resume data.
The system ingests PDF, DOCX, and raw text, runs it through a 5-stage enrichment pipeline powered by LLM, and returns a fully normalized, taxonomy-mapped candidate profile in under 3 seconds.
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WHAT THE SYSTEM ACTUALLY DOES
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↳ Company Normalization
Resolves entity aliases at extraction time — "Goog", "IBM Corp", "MSFT Azure", "Big Blue" all resolve to canonical names. Acquisition chains are detected and flagged.
↳ Skills Taxonomy Mapping
Abbreviations expanded at parse time (K8s → Kubernetes, TF → TensorFlow, PG → PostgreSQL). Skills cluster into multiple domains
↳ Education Intelligence
School names normalized to canonical form. Institutions tiered as Elite / Target / Standard — covering Ivy League, MIT, Stanford, Caltech, CMU, and equivalent global institutions.
↳ Confidence Scoring
Every extracted field carries a 0.0–1.0 confidence score. Parsing ambiguities are surfaced as structured flags, not silent failures.
The system ingests PDF, DOCX, and raw text, runs it through a 5-stage enrichment pipeline powered by LLM, and returns a fully normalized, taxonomy-mapped candidate profile in under 3 seconds.
────────────────────────
WHAT THE SYSTEM ACTUALLY DOES
────────────────────────
↳ Company Normalization
Resolves entity aliases at extraction time — "Goog", "IBM Corp", "MSFT Azure", "Big Blue" all resolve to canonical names. Acquisition chains are detected and flagged.
↳ Skills Taxonomy Mapping
Abbreviations expanded at parse time (K8s → Kubernetes, TF → TensorFlow, PG → PostgreSQL). Skills cluster into multiple domains
↳ Education Intelligence
School names normalized to canonical form. Institutions tiered as Elite / Target / Standard — covering Ivy League, MIT, Stanford, Caltech, CMU, and equivalent global institutions.
↳ Confidence Scoring
Every extracted field carries a 0.0–1.0 confidence score. Parsing ambiguities are surfaced as structured flags, not silent failures.
AI Algorithms
Convolutional Neural Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Recurrent Neural NetworkAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI-Enhanced Classification, AI-Generated Code, Conversational AI, Image Analysis, Image Processing, Image RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, NVIDIA AI Platform, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, OpenAI Codex, Stable DiffusionWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$550
|
|---|---|---|---|
| Delivery Time | 4 days | 6 days | 10 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code | - | - | - |
6 reviews
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RJ
Rohit J.
Jul 5, 2026
AI Developer (Remote)
RJ
Rohit J.
Jun 9, 2026
Remote AI Developer (India / Pakistan / Bangladesh) – Node.js, AI Apps, Prompt Engineering, GCP
HI
Hendy I.
Apr 20, 2026
Teach ComfyUI I2V using Alibaba Wan2.1 and Wan2.2 in Zoom meeting (hourly pay)
Excellent tutor, very detailed and clear.
GB
Goergi B.
Dec 4, 2025
Develop 1 mobile app "DotDashDelirium" Unity/Flutter for Android and IOS
Thank you, Muhammad! You did a great job with my assignment and created a wonderful project for me. Thank you for being so responsible and staying in touch with me so that we could quickly resolve any issues!
AK
Amir K.
Mar 26, 2024
Create a construction management app
I wanted my app in a very short period of time and also i want it to be customised as well...and thanks to talha, he really delivered it all in time and according to my expectations as well...So indeed he is a very hard working and talented person and would recommend him to others as well for any job
About Muhammad
Senior Ai Engineer | AI Agent Developer | AI Automation Expert
100%
Job Success
Islamabad, Pakistan - 4:06 pm local time
I build autonomous AI agents, LLM pipelines, and n8n automation workflows — systems that read documents, make decisions, call APIs, and take action without constant human input. My work lives in production, not in Notion docs.
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⚡ WHAT I'VE ACTUALLY SHIPPED
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🔹 PropAgent.AI — Autonomous property investment agent that sources, analyzes, and scores real estate opportunities in real time
🔹 MedLex AI — Legal intake automation system; AI reads case details, extracts key facts, and routes to correct workflow — replacing hours of manual intake work
🔹 Crime Research Agent — AI pipeline that processed and structured 46 case files in under 2 minutes
🔹 Deal Flow Intelligence System — LLM + RSS + SEC filings pipeline for automated investment research and signal detection
🔹 AI WhatsApp Ordering System — High-performance ordering bot built on Groq + LLaMA 3.3 + n8n; handles orders, confirmations, and CRM updates end-to-end
🔹 Real Estate Voice Agent — LiveKit-based voice AI with RAG knowledge base + HubSpot CRM integration; books appointments autonomously
🔹 Autonomous SEO Content Pipeline — LLM + external API integration generating clinical health content at scale
🔹 AI Resume Parsing Pipeline — LLM-powered extraction, entity normalization, and structured output from unstructured CVs
🔹 Generative AI (ComfyUI + RunPod) — LoRA training, Flux image generation, serverless deployment for e-commerce (Shopify try-on, product variation generation)
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🛠 CORE CAPABILITIES
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AI Agents & Automation
n8n, Make . com, CrewAI, AutoGen, multi-agent orchestration, autonomous research agents, document processing pipelines
LLMs & RAG
LangChain, LlamaIndex, OpenAI (GPT-4o), Claude, Groq, LLaMA 3, Mistral, Pinecone, ChromaDB, FAISS, Qdrant — from simple Q&A to enterprise knowledge retrieval
Voice AI
LiveKit, Vapi, Retell AI, ElevenLabs, Deepgram — full voice agent pipelines with CRM and calendar integration
Generative AI & Image
ComfyUI, RunPod (serverless), Flux, LoRA/QLoRA fine-tuning, SDXL, virtual try-on, product image generation
Integrations
WhatsApp Business API, HubSpot, Shopify, Google Sheets, Notion, Airtable, Slack, REST APIs, Webhooks, SEC/RSS feeds
Backend
Python, FastAPI, Node Js, Next Js, Nest Js, Firebase, Supabase PostgreSQL, MongoDB, Docker, AWS, serverless architecture
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✅ GOOD FIT
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✔ You have a real workflow you want automated or AI-augmented
✔ You need something production-ready — not a prototype
✔ You want one person who can own the whole system, end to end
✔ Speed and reliability matter more than the cheapest quote
❌ NOT A FIT
✖ You want a ChatGPT wrapper built in a weekend
✖ You're still exploring AI with no defined use-case
✖ Price is the only variable in your decision
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💬 Let's talk. Tell me what's eating your team's time — I'll tell you exactly what an AI system can do about it, and what it'll take to build.
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#AIEngineer #AIDeveloper #AgenticAI #AIAgents #GenerativeAI #LLM #LLMEngineer #OpenAI #ClaudeAI #LangChain #LangGraph #CrewAI #RAG #MCP #PromptEngineering #AIAutomation #WorkflowAutomation #Python #FastAPI #VectorDatabases #AIChatbots #MachineLearning #DeepLearning #NLP #ComputerVision #AIIntegration #APIDevelopment #CloudAI #ArtificialIntelligence #CustomAISolutions
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture Development
Execution & Implementation



