Sarah Faisal Sikandar
Ismat & Mustafa Kassam HU TOPS Scholar
Aspiration Statement
A final-year student passionate about AI engineering, focused on LLM fine-tuning, AI agents, and alignment. Eager to build intelligent, reliable, and responsibly designed systems as I begin my professional journey.
Core Skills
- AI Agent Development
- Ensemble Learning & Data Augmentation
- LLM & Prompt Engineering
- Natural Language Processing
- Python & ML Pipelines
Core Competencies
- Planning
- Takes Initiative
Preferred Career Paths
First priority: AI Engineer
Second priority: ML Engineer
Third priority: AI Research Engineer
Experience
Leadership / Meta-curricular
- Orientation Leader
- Operations Lead, Serve Club
- Vice Chair- Events Cabinet, Habib University Student Government
- Table Tennis Representative, Sports & Recreational Club
Internship / Volunteer Work
- Ai Engineer Intern, Improdata (March – May 2026)
- Ai Engineer Intern, Folio3 (September 2025 – March 2026)
Publications / Creative Projects
- Research Paper – Sexism Identification in Tweets Using Ensembles & Augmentation: A Multilingual Approach Accepted at EXIST Lab
- Conference Presentation – CLEF (Conference and Labs of the Evaluation Forum) 2025 (Madrid, Spain)
- Publication – https://lnkd.in/d_B34wvH
Final Year Project
Project Title
AI-Cruit
Description
AICruit is an AI-driven recruitment automation platform developed as a final-year Kaavish project at Habib University, in collaboration with Folio3 Pvt. Ltd. The project aims to address the inefficiencies and inconsistencies of manual hiring processes by integrating large language models into an end-to-end recruitment pipeline. The system automates resume parsing, candidate evaluation, and adaptive interview generation — tailoring questions to each candidate's profile. Responses are automatically transcribed, analyzed, and scored, providing recruiters with structured evaluation reports. An automated proctoring feature ensures interview integrity. The platform's key benefit is significantly reducing recruiter workload while improving consistency and fairness in candidate assessment, ultimately making the hiring process faster, smarter, and more reliable.