AI Engineer with 2+ years in Generative AI & Machine Learning.
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Graduate in Artificial Intelligence and an entry-level GenAI ML engineer with production experience building and operating LLM pipelines. Skilled at end-to-end delivery retrieval design, prompt orchestration, fine-tuning, and secure API deployment for real-world applications.
National University of Computer and Emerging Sciences (FAST)
Bachelor's · Artificial Intelligence
August 1, 2021 – June 30, 2025
Forman Christian College (FCCU)
F.Sc Pre-Engineering
June 1, 2019 – May 31, 2021
Solar Citizen
Junior AI Engineer
August 1, 2025 – Present
India
YoungDev Interns
Machine Learning Intern
March 1, 2025 – May 1, 2025
India
Rounders UK
Software QA
September 1, 2024 – July 1, 2025
India
Freelance Client (Klmentor.ai)
AI Mentor & Developer
May 1, 2024 – December 1, 2024
India
BRAG AI – Production Legal Document Engine
June 24, 2026 – Present
Architected a document generation engine capable of drafting 20+ page confidential agreements (Leases, NDAs, Employment Contracts) with strict Pakistani legal formatting and template-aware DocGen. Solved long-form context loss by building a structured Copy-Safe HTML → MS-Word pipeline and implemented hybrid RAG with intent routing (draft vs fact-check). Implemented passage scoring, session memory, and grounded citations to minimize hallucinations and ensure auditability.
Advanced Medical Report Generation with MLOps
June 24, 2026 – Present
Built a classification and generation pipeline with BLEU/METEOR/ROUGE metrics and per-label precision/recall/F1 tracking for medical report quality. Implemented automated training, deployment, monitoring, and versioned datasets/models for reproducibility and auditability. Delivered doctor-readable templated drafts with audit trails for clinical review and integration into downstream workflows.
MindSphere - Crisis-Aware AI Mental Health Companion (FYP)
June 24, 2026 – Present
Built a full-stack GPT-4o-based mental health support system with a priority safety architecture; explicitly designed to assist users without diagnosis or medical claims and to escalate emergencies when needed. Designed the First Responder Protocol (regex + semantic similarity models) to detect crisis intent and method indicators, trigger emergency escalation flows, and log safety events for audit and review. Implemented a hybrid RAG pipeline (BM25 + dense vector search + FlashRank re-ranking) with dynamic prompt injection and persona-aware responses; developed LEAS (ROBERTa emotion classification with sigmoid smoothing), journaling, personalization, recommendations, and an accessible WebAudio breathing engine.
Job Finder LLM – RAG Job Search Agent
June 24, 2026 – Present
Built a RAG pipeline that scrapes LinkedIn/Indeed, normalizes postings to JSON, and retrieves opportunities by semantic constraints (skills, seniority, location). Integrated DeepSeek-R1-distill (GroqAPI) to rank and summarize roles; optimized prompts for structured JSON, deduplication, and justification. Deployed a FastAPI backend with parameterized search and top-K retrieval to serve consistent schema responses to UI integrations.
TensorFlow for Deep Learning
Unknown
June 1, 2026 – Present
Deep Learning Specialization (Ng)
Unknown
June 1, 2026 – Present
LLMs with LangChain (DataCamp)
DataCamp
June 1, 2026 – Present
Building AI Browser Agents
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a diverse range of applications, from legal document generation to medical reporting and mental health support, indicating adaptability and a broad interest in AI's impact. Their academic background in Artificial Intelligence from a reputable institution, coupled with practical experience in both professional and personal projects, suggests a strong drive for continuous learning and application. The freelance and internship experiences, alongside a current Junior AI Engineer role, demonstrate a proactive approach to gaining varied industry exposure. The emphasis on auditability, safety, and user-centric design aligns well with responsible AI development practices.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project descriptions, tackling complex issues like long-form context loss and hallucination minimization. Their experience with Kanban boards and sprint management indicates an understanding of agile methodologies and operational workflows. The focus on auditability, reproducibility, and quality metrics (BLEU/METEOR/ROUGE) suggests a detail-oriented and quality-conscious approach to development. The MindSphere project highlights an ethical consideration in AI development, particularly regarding user safety and crisis management.