I'm a Computer Science Engineering student (4th year, ITS Engineering College – AKTU) focused on backend engineering, AI/ML systems, and full-stack product development. I like turning messy, real-world data into systems that are fast, reliable, and actually usable — from carbon-emission routing engines to NLP-driven resume screening tools.
I've worked through simulation programs with J.P. Morgan Chase & Co. (Quantitative Research & Software Engineering) and Deloitte Australia (Data Analytics), and I build end-to-end projects in my own time that combine Python, cloud infrastructure, and applied ML.
Currently:
Learning: Distributed Systems, System Design at scale
Building: AI-powered developer & productivity tools
Exploring: LLM-integrated backend architectures
Open to: SDE Internships, AI/ML Engineering roles, Open Source collaborationLanguages
Backend & Databases
Cloud & Tooling
Data & ML Libraries
| Domain | Proficiency | Details |
|---|---|---|
| NLP & Text Processing | Intermediate | Keyword-extraction pipelines, custom text-parsing algorithms for resume/job-matching |
| ML Fundamentals | Intermediate | Scikit-Learn, model evaluation, feature engineering |
| LLM Integration | Intermediate | Structured prompt engineering with Google Gemini API for real-time data parsing |
| Data Analysis | Advanced | Pandas/NumPy pipelines, statistical analysis, KPI extraction on large datasets |
🌍 Sustainable Routing Optimization System (SROS)
Full-stack web app that calculates real-time carbon emissions for transit routes and suggests eco-friendly alternatives, with a Flask backend handling concurrent API requests.
| Aspect | Detail |
|---|---|
| Stack | Python, Flask, SQLAlchemy, Google Gemini API |
| Scale | Real-time multi-user API request handling |
| Performance | ~20% reduction in data-processing overhead via LLM-assisted parsing |
| Data Layer | Relational schema via SQLAlchemy for low-latency route-history lookups |
| Repository | SROS-main → |
Uses Gemini for structured prompt-based parsing of transit datasets, and a normalized relational schema to keep routing-history lookups fast and consistent.
📄 AI-Powered Resume Analyzer
End-to-end NLP system that extracts technical skills from resumes and matches candidates against job descriptions automatically.
| Aspect | Detail |
|---|---|
| Stack | Python, NLP, Scikit-Learn, (MERN-based ATS variant also built) |
| Scale | Batch resume parsing + ATS-style scoring |
| Performance | ~50% improvement in screening efficiency |
| Impact | Custom keyword-matching + text-parsing pipeline for JD-candidate fit |
| Repository | AI-resume-analyzer → |
Combines classic NLP techniques with Gemini-powered parsing (MERN variant) to generate ATS compatibility scores.
📊 Sales Insights & Relational Database Analytics
Data analytics engine that queries and monitors large-scale historical sales KPIs via a star-schema ETL pipeline.
| Aspect | Detail |
|---|---|
| Stack | Python, SQL, Power BI |
| Scale | 500K+ sales transactions |
| Performance | ~40% reduction in manual processing overhead |
| Impact | Optimized query design for revenue-trend and product-performance benchmarking |
| Repository | Sales-insights-analytics → |
Quantitative Research & Software Engineering Simulation J.P. Morgan Chase & Co. — Jan 2026
Engineered programmatic financial-data solutions in Python, optimizing algorithmic execution for high-frequency data streams under production-like constraints, with a strong emphasis on fault tolerance and reliability.
Python Software Engineering Fault Tolerance Scalability
Data Analytics Virtual Internship Deloitte Australia (via Forage) — Feb 2026
Built interactive Tableau dashboards from raw business datasets for executive-level performance tracking, and ran statistical/algorithmic data cleaning in Excel to surface business-critical KPIs.
Tableau Data Cleaning Statistical Analysis KPI Reporting
| Recognition | Details |
|---|---|
| AWS Certified Solutions Architect – Associate | Cloud architecture & infrastructure design |
| OCI 2025 Data Science Professional | Oracle Cloud Infrastructure |
| Microsoft Data Analytics & AI Certification | Applied data analytics + AI fundamentals |
AWS
Oracle
Microsoft
IBM
Cisco
Note: the snake animation requires a one-time GitHub Actions workflow set up in a repo named exactly adityabansal-tech (see setup note below).
"Good code is a solution to a problem that hasn't happened yet."