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AI Engineer with less than a year in Agentic AI Systems & LLMs
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Muhammad Nouman Rashid is an aspiring AI Engineer with 6 months of hands-on experience in developing and deploying AI-powered solutions. He possesses expertise in Agentic AI Systems, LLMs (Llama 3.3 70B), LangGraph, and web scraping with Playwright. His work includes creating intelligent chatbots, resume screening tools, and legal technology platforms, demonstrating a strong ability to build end-to-end AI applications and integrate various APIs for enhanced functionality. With a background in Python, JavaScript, and a strong foundation in Machine Learning and Deep Learning, he is passionate about leveraging AI to automate complex workflows and drive impactful innovation.
NUML University
Bachelor of Science · Artificial Intelligence
August 1, 2022 – June 30, 2026
Higher Education Commission (HEC), PM Youth Dev. Centre
AI Developer Intern
January 1, 2026 – June 1, 2026
Islamabad, Islamabad Capital Territory, Pakistan
ClinicOS
January 1, 2026 – June 1, 2026
Developed an AI-driven clinic management platform that automates appointment booking, scheduling, and front-desk patient communication via WhatsApp. Integrated Twilio's WhatsApp Business API with Groq's Llama 3.3 70B model to enable natural, real-time conversational interactions with patients. Built the backend with Node.js and Supabase for real-time data synchronization, and a Next.js dashboard for administrators to manage appointments and patient records. Deployed the system using Railway (backend) and Vercel (frontend) to support production traffic at scale. Reduced manual front-desk workload by automating routine scheduling tasks and patient inquiries.
TenderMind
January 1, 2026 – June 1, 2026
Built a system that aggregates government tender and contract listings across multiple Pakistani sources, including PPRA Federal, Sindh PPRA, Punjab ePROC, and WAPDA. Used Playwright to scrape JavaScript-heavy government portals and extract structured tender data. Applied Groq and LLaMA3 to analyze scraped listings and match relevant tenders to individual business profiles. Automated generation of tender summary PDFs and ready-to-use Word proposal drafts for matched opportunities. Implemented WhatsApp notifications via Twilio to alert businesses in real time when a relevant tender is identified. Architected the platform with multi-tenant support, allowing multiple businesses to use the system concurrently, and scheduled fully automated daily runs via Railway and Windows Task Scheduler. Resolved recurring production issues including Groq API instability, JavaScript rendering failures, and Windows scheduling bugs, and migrated functionality when a third-party dependency was deprecated.
LexOS
January 1, 2026 – June 1, 2026
Built an AI-powered platform to analyze contracts and legal documents, trained on a dataset of 300+ clause types spanning over 30,000 labeled examples. Implemented a semantic search system using ChromaDB and sentence-transformer embeddings to retrieve relevant legal clauses by meaning rather than keyword. Engineered an adaptive response layer powered by an LLM that delivers concise answers for simple queries and in-depth risk analysis for complex legal questions. Designed a dual-output reporting system generating full English reports alongside plain-language Roman Urdu summaries to improve accessibility for non-expert business owners. Built a batch-processing pipeline enabling bulk contract uploads and automated generation of professional PDF reports via ReportLab. Created a clause relationship map visualizing connections between clause types and document clusters within the database.
Cultural Fit Analysis
The candidate's projects show a strong inclination towards building practical, impactful AI solutions, often addressing real-world problems (legal tech, clinic management, tender intelligence). This aligns well with a results-oriented, innovative culture. Their involvement in extracurricular leadership roles suggests a proactive and engaged personality, which can contribute positively to team dynamics. The diversity of their projects indicates adaptability and a broad interest in applying AI across different domains.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through resolving production issues and adapting to deprecated dependencies. Their project descriptions indicate an ability to work on complex, multi-faceted problems. Collaboration is evident from their internship experience where they gathered requirements and aligned platforms with organizational needs. Leadership roles in extracurricular activities suggest initiative and organizational skills.