AI ENGINEER · SYDNEY, AUSTRALIA

Bisar UlHasan

I build agentic and generative AI systems, and I help companies and people put AI to real work.

Focus: Agentic + GenAI
Stack: LLMs · RAG · Agents
Now: Building daily

LLMs/RAG Pipelines/Multi-agent/LangGraph/LangChain/MCP/Vector Search/Fine-tuning/FastAPI/PyTorch/Evaluation/Prompt Engineering/Vercel/Supabase/Docker/Python/LLMs/RAG Pipelines/Multi-agent/LangGraph/LangChain/MCP/Vector Search/Fine-tuning/FastAPI/PyTorch/Evaluation/Prompt Engineering/Vercel/Supabase/Docker/Python/

01 / About

I turn repetitive, slow work into AI systems that ship.

Growth-orientedSolution-firstShips fastGoes deep on the detail

I am an AI Engineer based in Sydney, working across agentic and generative AI: LLMs, multi-agent pipelines, RAG systems, and the data pipelines underneath them. My day job is driving AI adoption and automation inside a school, where the work is less about demos and more about systems people rely on every day.

Before this I helped build a speech-AI startup from a small founding team into a full company, leading dataset and model work for production text-to-speech. Earlier still, I did multimedia engineering for large institutional clients. Different tools each time, same instinct: find the slow, manual thing and build something that does it better.

I have been a gamer since 2009, and that is where the working style comes from. I stay in a problem until it is solved, and I go deep on the detail rather than moving on before I understand the failure. I default to what is possible, I pick things up fast, and I am usually shipping something new.

02 / Work

Real systems, shipped.

Problem, build, outcome — no fluff.

RAG / PRODUCTION01

Teaching Assistant Bot

Problem: Staff needed fast, trustworthy answers from a large, messy body of school documents, without sending anything to a third-party cloud.
Built: A production RAG system with hybrid retrieval, cross-encoder reranking, citation enforcement, and a CI-gated evaluation pipeline so quality is checked on every change. Runs locally on open-source models with a cloud fallback.
Outcome: Source-cited answers people can trust, with retrieval quality measured on a golden evaluation set rather than guessed at.

PythonLangChainWeaviateOllamaCohereRAGASGitHub Actions

View on GitHub

DOCUMENT AI02

Teaching Program Automation

Problem: Writing teaching programs across the school's full subject list was slow, repetitive, and ate weeks of staff time.
Built: An AI document-processing pipeline that drafts programs from source material and structures them to the required format, leaving staff to review rather than write from scratch.
Outcome: Turned a multi-week manual job into a review-only workflow, across the whole subject list.

PythonPlaywrightLLMs

MULTI-AGENT03

Meeting Minutes Pipeline

Problem: Turning raw meeting transcripts into clean, formatted minutes by hand was tedious and easy to get behind on.
Built: A three-agent pipeline — transcript processor, minutes writer using Anthropic tool calling, and document generator — exposed via a FastAPI webhook with async background execution.
Outcome: Raw transcripts become formatted Word minutes automatically, with a person only in the loop to approve.

PythonFastAPIAnthropicMulti-agent

DATA PRODUCT04

NAPLAN Analytics Platform

Problem: Raw national-assessment results were hard to turn into decisions, and analysing them by hand took weeks.
Built: A web platform that turns raw assessment exports into interactive dashboards for school-wide decisions, plus personalised practice that targets each student's weakest skills.
Outcome: Collapsed weeks of manual analysis into a near-instant view, and made the data usable by people who are not analysts.

ReactVercelSupabasePostgreSQLEdge Functions

03 / Testimonials

What people I've led and worked with say

It was an amazing experience to have Bisar as my team lead. He is an exceptional individual who can lead the team towards its goals. During his time at Scribe Audio he introduced his team with various tools and technology stacks which accelerated the operations.

Syed Haider Ali Zaidi

Building AI Platform @ Enmacc

Reported to Bisar directly

Read them on LinkedIn →

04 / AI focus

How I think about AI adoption

Start from the boring, repetitive work

The best AI wins are not flashy. They are the slow, manual tasks people do every week. Find one, automate it well, and the value is obvious.

Measure it or it does not count

An AI system you cannot evaluate is a demo. Build the evaluation set, gate quality in CI, and you can ship changes without crossing your fingers.

Keep a person in the loop where it matters

Automation should draft and propose. People approve. That split is what makes AI safe to put in front of a real organisation.

Let's build something with AI.

Whether you want to adopt AI in your business, ship a system, or just compare notes, my inbox is open.

bisar1000@gmail.com

in/bisarhasan bisarulhasan @bisarulhasan @bisarhasan