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Osama I. AI, Python and Cloud Platforms

My name is Osama I. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Python, LangChain, Pinecone, LLM Prompt Engineering, Vector Embedding, etc.. I hold a degree in . Some of the notable projects I’ve worked on include: AI Coaching System, Term Deposit Marketing Analysis, AI Chatbot Procurement, Unhappy Customers Predictive Analysis. I am based in Karachi, Pakistan. I've successfully completed 4 projects while developing at Softaims.

I thrive on project diversity, possessing the adaptability to seamlessly transition between different technical stacks, industries, and team structures. This wide-ranging experience allows me to bring unique perspectives and proven solutions from one domain to another, significantly enhancing the problem-solving process.

I quickly become proficient in new technologies as required, focusing on delivering immediate, high-quality value. At Softaims, I leverage this adaptability to ensure project continuity and success, regardless of the evolving technical landscape.

My work philosophy centers on being a resilient and resourceful team member. I prioritize finding pragmatic, scalable solutions that not only meet the current needs but also provide a flexible foundation for future development and changes.

Main technologies

  • AI, Python and Cloud Platforms

    4 years

  • Python

    3 Years

  • Langchain

    3 Years

  • Pinecone

    3 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Techlogix

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Experience Highlights

AI Coaching System

Developed a RAG-based AI coaching system for a U.S. client using Python, FastAPI, Groq, and Pinecone. Features include: 1. On‑Demand AI chat with internet search, 2. Failure‑Driven feedback with personalized analysis, 3. Skills Validation via case study & interview-style Q\&A, 4. Sentiment‑Driven coaching based on chat history. Built semantic retrieval, prompt workflows, Gradio UI, and real-time feedback logic. Led backend dev, reduced infra cost, and aligned features with learning goals.

Term Deposit Marketing Analysis

The Bottom Line: We built a two-stage ML system that captures 91% of potential term deposit subscribers while focusing your marketing resources on the highest-value segments: retirees, high-balance customers, and the 60+ age group. By prioritizing call quality over quantity (longer conversations = higher conversions) and limiting contacts to 2-3 per customer, you can maximize ROI and minimize wasted effort.

AI Chatbot Procurement

Developed an AI chatbot demo for Penny to answer procurement-related queries in natural language. Built using LangChain, OpenAI, and Pandas Agent, it supports both CSV and MongoDB data sources. The chatbot interprets questions about POs, suppliers, budgets, and timelines. Includes a simple Tkinter GUI for non-technical users and modular notebooks for testing and prompt tuning. Designed to showcase how AI can simplify data access for procurement teams.

Unhappy Customers Predictive Analysis

Developed a predictive analytics demo to identify unhappy customers using machine learning. This project analyzes customer feedback data (calls, surveys, support tickets) to predict dissatisfaction before it escalates. It includes: • Data preprocessing and feature engineering • Binary classification models (Logistic Regression, SVM) with TF‑IDF text features • Model evaluation using accuracy, Cohen’s kappa, MCC, and MSE • Jupyter notebooks for training, tuning, and performance comparison • Data visualization of complaint patterns over time

Education

  • NED University of Engineering and Technology

    in Bachelor in Computer Systems Engineering

    2017-01-01-2021-01-01

Languages

  • Arabic
  • English
  • Urdu

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