$ whoami
# I build production-ready AI applications — RAG systems, LLM pipelines, and intelligent backends that solve real problems.
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Professional Roles
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AI Projects
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CGPA
0 Year
AI Experience
[01] // Core Expertise
01
Prompt and context engineering, structured outputs, tool calling, and LLM evaluation for real-world applications
02
Vector and vectorless retrieval, embeddings, and semantic search using FAISS, Chroma, and Pinecone
03
Agent orchestration and workflows with LangGraph and CrewAI, backed by FastAPI, Docker, and AWS
[02] // About Me
I'm a BS Computer Science student in my 7th semester at Govt College University Faisalabad, with around 1 year of hands-on experience in Machine Learning and Generative AI.
I started as a Machine Learning Engineer at WebTech.dev (Vehari), and then worked Remotely as a Freelance AI Engineerbuilding multi-agent systems, RAG pipelines, and production LLM applications.
I design agentic AI workflows with LangGraph and CrewAI, and ship them end-to-end with FastAPI, Docker, and AWS from idea to production.
Location
Burewala, Pakistan
Education
B.Sc. CS (7th Sem)
Experience
Freelance AI Engineer
01
Foundations, classification, regression
02
CNNs, RNNs, LSTMs, word embeddings
03
Prompt engineering, model integration
04
Vector search, embeddings, retrieval
05
Multi-agent, tool use, orchestration
06
Model adaptation, deployment, monitoring
[03] // Technical Skills
01
Prompt engineering, vector & vectorless RAG, multi-agent systems, and LoRA/QLoRA fine-tuning — the core of everything I build.
$ 4 skills loaded ▊
02
Orchestrating stateful agents with LangGraph and CrewAI, backed by FAISS, Chroma, and Pinecone for semantic retrieval.
$ 5 skills loaded ▊
03
End-to-end ML pipelines — data cleaning, feature engineering, training, and evaluation — from Scikit-learn to gradient boosting and TensorFlow.
$ 4 skills loaded ▊
04
Production-oriented Python APIs with FastAPI — REST endpoints, SSE streaming, and fast data layers with SQL, PostgreSQL, and Redis.
$ 7 skills loaded ▊
05
Containerized deployments with Docker, CI/CD via GitHub Actions, and cloud infrastructure on AWS — the stack that runs my AI apps in production.
$ 3 skills loaded ▊
06
The daily toolkit — Pandas & NumPy for data, Matplotlib & Seaborn for insight, Git/GitHub for version control, Streamlit & React for UIs.
$ 8 skills loaded ▊
[04] // Featured Projects
Building intelligent systems that combine LLMs, RAG, and automation.
AI Assistant
Description
Multi-agent software engineering platform where AI agents plan, implement, review, test, and deploy software projects — with stateful workflows, persistent memory, tool calling, Git checkpoints, retry/recovery, and human-in-the-loop controls.
Multi-Agent Systems
Description
Agentic career intelligence platform built with LangGraph and FastAPI — resume analysis, job discovery, job matching, ATS evaluation, career planning, and interview preparation, combining LLM reasoning with deterministic skill matching and ATS scoring, deployed with Docker and AWS.
Multi-Agent Systems
Description
Multi-agent startup validation system running parallel competitive, market, and risk analysis with quality-control and automatic retry workflows, plus web research, structured state management, validation gates, and multiple LLM providers.
GenAI
Description
Freelance academic project that classifies news articles as real or fake using LLM-based analysis built with LangChain. Includes confidence scoring and text preprocessing pipeline.
[05] // Professional Experience
Freelance
Built multi-agent AI systems using LangGraph and CrewAI to orchestrate complex, multi-step workflows with stateful execution.
Developed and optimized RAG systems, implementing both vector-based and vectorless architectures for intelligent data retrieval.
Designed LLM-powered applications with structured outputs, tool calling, retrieval workflows, and production-oriented backend APIs.
WebTech.dev Software House, Vehari
Built end-to-end machine learning pipelines covering data cleaning, EDA, feature engineering, model training, evaluation, and deployment using Scikit-learn, XGBoost, and LightGBM.
Developed NLP solutions for text classification and sentiment analysis with HuggingFace Transformers, exposing trained models via FastAPI REST endpoints for real-time client use.
[06] // Achievements
01
Secured 4th position in the on-the-spot programming competition at Byte & Battle, competing against participants from multiple universities.
02
Secured 2nd position in VSpark, the on-the-spot programming competition held at COMSATS University Vehari.
[07] // Contact
Open to internships, freelance projects, and full-time opportunities in AI engineering and development.
