AI Engineer with less than a year in Generative and Agentic AI
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Aspiring AI/ML Developer with hands-on experience building Python-based machine learning pipelines, LLM-integrated applications, and agentic AI workflows. Proficient in deep learning frameworks (PyTorch / TensorFlow), the OpenAI API ecosystem, and modern MLOps tooling. Adept at translating research concepts into production-ready systems and experienced bridging ML back-ends with clean React/Node front-ends. Currently completing a B.Sc. in Computer Science (graduating 2026) while actively building a portfolio of generative and agentic AI projects.
University of Agriculture Faisalabad
BS Computer Science · Computer Science
August 1, 2022 – June 30, 2026
PAEC Model College for Boys Chashma
Intermediate · Pre-Medical
June 1, 2016 – May 31, 2019
BugMonks
AI Integration Engineer (Intern)
July 1, 2025 – September 1, 2025
Lahore, Punjab, Pakistan
Python ML Experiments Portfolio
January 1, 2026 – April 1, 2026
Completed 100 Days of Python (Udemy) with dedicated ML modules covering NumPy, Pandas, scikit-learn classification / regression, and neural network fundamentals. Built and fine-tuned text classification models using Hugging Face Transformers; documented experiments in Jupyter Notebooks with loss curves and evaluation metrics. Exploring multi-agent orchestration patterns (AutoGen / LangChain Agents) to automate research-summarisation and code-generation tasks.
AI-Powered Timetable Management System
September 1, 2025 – November 1, 2025
Designed an agentic scheduling engine using LangChain and the OpenAI API that autonomously resolves room and lecturer conflicts across multiple university departments. Implemented a retrieval-augmented generation (RAG) layer to ingest department constraints as context, enabling the LLM to produce valid, conflict-free timetables in a single pass. Built a full-stack interface (React + Node.js REST API) achieving 99% UI consistency across major browsers, empowering academic staff to manage schedules without manual intervention.
100 Days of Python
Udemy
June 1, 2026 – Present
React.js
SuperSimpleDev
June 1, 2026 – Present
Generative AI with LLMs
DeepLearning.AI
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in AI, particularly generative and agentic AI, which aligns well with an AI Engineer role. The academic project on timetable management and personal ML experiments portfolio show initiative and a drive to apply learned concepts. The internship experience at BugMonks further solidifies practical application of AI. The breadth of skills across AI/ML, front-end, and databases suggests adaptability. However, the experience is primarily academic and internship-level, which might require mentorship in a fast-paced industry setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems (e.g., conflict resolution in timetabling) and collaborate (e.g., prompt engineering with senior engineers). The '100 Days of Python' and 'Generative AI with LLMs' certifications suggest a proactive learning attitude. However, without specific psychometric or English test scores, a comprehensive assessment of communication clarity, logical reasoning, work attitude, stress handling, and team collaboration is limited. The leadership role in a university society indicates potential for teamwork and initiative.