I build AI systems that solve human problems.
I think harder about why than how.
I'm Aarav, a 21 year old AI engineer from India.
I don't build things to add them to a resume. I build them because something bothered me: a problem felt unsolved, a system felt broken, a better way felt obvious.
Right now I'm finishing my undergrad and shipping production-grade AI systems. Not demos. Not tutorials. Things that actually run.
Not concept work. Not placeholder case studies. These are the builds where the system, the interface, and the product logic had to hold together under real use.
A bank statement intelligence system that turns messy PDFs and scans into categorized, validated Excel output for real finance operations.
Document ingestion, extraction, validation, and export layered into one dependable internal workflow.
A live crypto forecasting workspace combining streaming market data, sequence models, and visual backtesting in one product surface.
Market streams, forecasting models, confidence intervals, and clean charting tied into a single analysis loop.
A resume screening platform that scores candidates semantically instead of relying on brittle keyword filters.
Resume parsing, semantic scoring, and recruiter-facing analysis views built for better hiring decisions.
A more ambitious system is underway. Not ready to show yet, but the direction is bigger, sharper, and more product-heavy.
The next build is intentionally quiet for now.
A focused stack built around shipping real products fast and correctly.
The languages I reach for when I need performance, product speed, or a backend that stays readable under pressure.
Python anchors most of the AI and backend work. TypeScript and SQL carry product and data contracts cleanly.
Interfaces that feel considered, move cleanly, and stay connected to the backend contract instead of floating above it.
I care about the final surface as much as the system underneath it, especially when product clarity and motion matter.
APIs, queues, databases, and infra choices built around reliability, speed, and not painting the product into a corner.
This is the layer that makes shipping fast possible: clean data models, sane service boundaries, and deployment that behaves.
Model selection, agent orchestration, NLP, retrieval, and the tooling layer that turns AI features into working products.
This is where I connect model capability to product usefulness: orchestration, embeddings, evaluation, and inference patterns that behave in production.
I only take on work I can own end to end. These are the systems I know how to ship, harden, and make useful.
From messy inputs to reliable AI workflows with clear system boundaries.
Production backends where models do real work: retrieval, reasoning, validation, and structured output.
Products that ship cleanly from schema to surface without losing coherence on the way up.
I own the full path: backend contracts, frontend interaction, deployment, and the final feel of the product.
Raw PDFs, scans, and statements turned into clean, auditable structured output.
Vision models, extraction pipelines, and validation layers built for operational use instead of demos.
Language models woven into existing products so the workflow improves without the product breaking.
I integrate AI where it belongs: search, copilots, automation, and internal tools that respect the rest of the stack.
I build production grade AI systems, sharp product surfaces, and workflows that still feel good when real people start leaning on them every day.