STUTI
NAINWAL
I build systems that scale —
from APIs to AI pipelines
Full-stack engineer focused on performance, clean architecture, and turning complex data into usable products. M.CS · University of Adelaide (GPA 6.8/7.0)
Projects
Slack Clone
A full-stack real-time messaging platform built entirely from scratch without any frontend frameworks, inspired by Slack...
EventSphere
A secure full-stack event management platform with organisation-scoped multi-tenant data isolation, two-factor authentic...
SplitMateEZ
A cross-platform mobile application integrating machine learning and LLM APIs to automate utility usage tracking, fair b...
Multimodal RAG Pipeline
A full multimodal retrieval-augmented generation pipeline enabling conversational querying of PDF documents using natura...
AI & ML Projects
A collection of end-to-end machine learning and deep learning projects built in Python using Jupyter Notebooks. Each pro...
Experience
Software Engineer Intern
Contensify- Built and deployed Search Signal — a live visual SEO reporting web application at searchsignal.contensifyhq.com
- Developed frontend features in vanilla JavaScript and CSS — data visualisations, filtering, and responsive reporting views
- Built backend services in Python and Node.js to fetch, process, and aggregate search performance data at scale
- Integrated Google Search Console API to pull live metrics including impressions, clicks, CTR, and average position
Teaching Assistant
University of Adelaide- Taught Event-Driven Computing and Algorithm Design and Data Structures (Java) to graduate cohorts
- Mentored 200+ students through code reviews, debugging, and one-on-one technical guidance
- Built automated grading scripts in Python that cut assessment turnaround by 50%
- Led weekly lab sessions on coding, debugging, performance optimisation, and software design patterns
Automation Engineer
TravClan- Architected end-to-end regression, UI, unit, and integration test automation frameworks cutting manual testing by 45%
- Built Postman API automation suites covering 35+ REST endpoints
- Conducted security testing identifying 12 vulnerabilities including XSS, SQL injection, and broken authentication
- Designed modular, reusable test components across a microservices architecture
Skills
Languages
Frontend
Backend & APIs
Databases
AI & ML
DevOps & Tools
Research & Reports
AI-Driven Personalisation in Education: A Study using LLMs and LSTM Models
This thesis investigates the role of AI in enabling personalised education through the integration of Large Language Models and sequential learning techniques. It covers simulation-based learning environments, adaptive feedback generation, and ethical considerations such as fairness and transparency. The research highlights the potential of scalable, privacy-aware architectures for next-generation educational technologies.
Large Language Model (LLM) for AI Education
This research explores the role of Large Language Models (LLMs) in enhancing personalized education. It highlights how AI can improve student engagement, automate administrative tasks, and deliver adaptive learning experiences. The study uses a mixed-methods approach, combining literature review, surveys, and interviews to evaluate effectiveness. It also addresses key ethical concerns such as data privacy and bias in AI systems. Overall, the research emphasizes the need for responsible and equitable integration of AI in education.
A Study on Transformer-Based NLP Approaches for Automated Educational Feedback Systems
This study explores how transformer-based NLP models like GPT, BERT, and T5 can generate personalised, context-aware educational feedback. It proposes a framework using fine-tuned models and prompt engineering to deliver scalable, real-time feedback beyond traditional rule-based systems. The research highlights benefits such as improved student engagement and reduced educator workload. However, it also identifies challenges including bias, lack of real-world validation, and ensuring feedback quality. Overall, it emphasises the need for human oversight and further testing before practical deployment.
Let's Work Together
I'm actively looking for full stack, software engineering, AI engineering, and research roles in Australia. Full work rights on Subclass 485 until September 2028.