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Roshan Aryal

AI Engineer · Apprento.io · Auckland

Roshan Aryal

I find the hidden boundaries in deployed AI systems — runtime ceilings, inferential limits, and the decisions that determine who they actually serve.

Currently building HireIQ at Apprento.io while finishing my Bachelor of IT at Otago Polytechnic Auckland.

  • HireIQlive at Apprento.io
  • 88.0% R²Random Forest
  • 5 projectsall shipped
View workResume

Selected work

Each one shipped. Each one written up.

Quiz Tournament Platform preview
Web · 2024–25
Live

Quiz Tournament Platform

Three-role Spring Boot platform. Live tournament state via versioned polling — no websockets needed.

React · Spring Boot · PostgreSQL · JWT
Read case study
More on GitHub

Research & manuscripts

Two manuscripts currently under double-blind peer review.

Manuscript under review · 2026

Runtime limits in browser-native AI retrieval systems

Identifying where in-browser semantic search silently breaks as data scales, and what structural fix removes the failure mode entirely.

Manuscript under review · 2026

Causal inference in observational road-safety data

Testing whether severity-based risk inference holds up once exposure and reporting-completeness confounds are accounted for.

Full titles, DOIs, and code will be linked here once each review concludes.

About

Roshan Aryal

I’m an AI engineer at Apprento.io and a final-year Bachelor of Information Technology student at Otago Polytechnic Auckland (graduating December 2026). I prefer work that ships — every project below is live, with real users or real evaluation data behind it.

Most recent is HireIQ, the AI candidate-intelligence platform I’m building at Apprento — Claude-powered assessment reports that took recruiters 30–45 minutes manually, now generated in under 30 seconds with a human-in-the-loop review step. Before that, the work I keep coming back to is applied ML — the Road Accident Risk Predictor at 88.0% R² was the project that made it stick.

On the engineering side I work end-to-end — Python / FastAPI for the AI layer, React / Next.js on the front, Spring Boot or Node on the back, Firebase or Postgres for state. I lean toward fewer abstractions, fewer dependencies, and shipping the thing.

Currently — building HireIQ at Apprento.io alongside finishing my degree.

Skills

Reach for first
Python · FastAPI · Claude API (Anthropic) · scikit-learn · pandas · React · Next.js · PostgreSQL · Firebase
Comfortable in
JavaScript · Java · SQL · Node.js · Spring Boot · Tailwind CSS · Streamlit · Docker · AWS · Git
Currently exploring
LLM evals & guardrails · Agent tooling · TensorFlow time-series

Experience

Work

AI Engineer · Apprento.io

Apr 2026 – present

Building HireIQ, an AI candidate-intelligence platform that takes a CV plus an interview transcript and produces a client-ready assessment report in under 30 seconds — down from a 30–45 minute manual baseline. Stack: Python / FastAPI, Anthropic Claude API, React, Docker, AWS.

Education

Bachelor of Information Technology

Otago Polytechnic — Auckland International Campus

2024 – Dec 2026 (in progress)

Machine learning, software development, cloud computing

Timeline

  1. 2024

    Started Bachelor of IT

    Otago Polytechnic Auckland. Picked the ML / software-development / cloud track.

  2. 2025

    Year of shipping

    Four projects from idea to live URL — two ML, two web. The set that’s in the projects above.

  3. Apr 2026

    AI Engineer at Apprento.io

    Joined as AI Engineer at Apprento.io, building HireIQ — the candidate-intelligence platform. Moved from coursework into shipped product work.

  4. Aug 2026

    First research manuscript submitted for peer review

    Submitted a manuscript on runtime limits in browser-native AI retrieval to peer review. Currently under double-blind review.

  5. Dec 2026

    Expected graduation

    Open to full-time AI / ML / full-stack engineering roles in NZ and remote.

Contact

I’m open to graduate roles, contract work, and a good conversation. The fastest path is email.