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AI Engineer with less than a year in LLM Development & Cloud Deployments
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science graduate with hands-on experience in AI/LLM development, backend engineering, and cloud deployments. Skilled in building RAG pipelines, REST APIs, and containerized applications using Python, Django, LangChain, Docker, and AWS, with multiple live projects.
University of Engineering & Technology
Bachelor of Science · Computer Science
August 1, 2022 – June 30, 2026
Xavor Corporation
DevOps Intern
June 1, 2026 – Present
Lahore, Punjab, Pakistan
BizAssist AI
May 1, 2026 – Present
Built RAG chatbot using LangChain, LLaMA 3.1 & FAISS for accurate, PDF-grounded business customer support Designed the embedding and vector search system; fixed semantic retrieval failures using a MultiQueryRetriever with a custom Prompt Template, improving answer recall on edge-case queries. Engineered prompt workflows with missing-item fallback handling to maintain response quality across incomplete documents; deployed live on Streamlit Cloud on a 100% free stack.
View ProjectagriAuction
February 1, 2026 – Present
Developed a multi-tenant auction platform with REST APIs and role-based access control(RBAC) Secured the system using JWT authentication (SimpleJWT) with Refresh Token Rotation and Token Blacklisting to mitigate session hijacking Optimized database performance by reducing N+1 queries(201 → 2) using Django ORM techniques (select_related, prefetch_related)
View ProjectFlask App Deployment on AWS EC2
September 1, 2025 – Present
Containerized a Flask application with Docker and deployed to AWS EC2, establishing a repeatable Docker Hub workflow that reduced deployment steps by approx. 60%.
View ProjectAWS Cloud Technical Essentials
Coursera
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in modern technologies, particularly in AI/LLM and cloud deployment, which aligns well with an innovative and fast-paced technical culture. The diversity of projects (AI chatbot, multi-tenant auction platform, Flask deployment) shows a breadth of technical curiosity and a willingness to tackle different challenges. The focus on practical, deployed solutions (live links provided) suggests a results-oriented mindset. However, the candidate is still pursuing a Bachelor's degree and has limited professional experience (DevOps Intern), which might indicate a need for mentorship and structured guidance in a senior role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on practical deployment. The 'BizAssist AI' project, in particular, highlights an ability to identify and resolve complex issues like semantic retrieval failures and manage prompt workflows for quality control. The deployment on a '100% free stack' suggests resourcefulness. However, without direct interview data, it's difficult to assess communication, teamwork, or stress handling abilities beyond what's implied by project completion.