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I'm an AI engineer with a Master of Artificial Intelligence (Monash University) and hands-on production experience building LLM-integrated applications and document-processing pipelines. Most relevant to this project: I've built end-to-end AI systems that ingest, analyse, and act on structured and unstructured data — including a live FastAPI + Vue.js application integrating Meta LLaMA 3.3 via Groq API for intelligent document understanding and NLP-driven outputs. I'm experienced in Python, FastAPI, REST API development, and deploying AI into production environments with real users. For this R&D Tax Incentives platform specifically, I can contribute across: → Document ingestion and data extraction pipelines (NLP, OCR, LLM-based parsing) → AI models for comparing extracted findings against legislative criteria → Automated report generation with qualification likelihood scoring → FastAPI or Django backend with secure file upload and data handling → Clean technical documentation and stakeholder-ready outputs I'm available immediately, work remotely from Melbourne, Australia (UTC+10), and can engage across Hong Kong business hours. While I don't have direct HK regulatory experience, I'm a fast researcher — my track record includes independently picking up unfamiliar domain requirements and delivering within tight timelines. Portfolio: github.com/aryabhardwaj23 Contact: _Email not available. Sign in: https://www.twine.net/signup_…I'm an AI engineer with a Master of Artificial Intelligence (Monash University) and hands-on production experience building LLM-integrated applications and document-processing pipelines. Most relevant to this project: I've built end-to-end AI systems that ingest, analyse, and act on structured and unstructured data — including a live FastAPI + Vue.js application integrating Meta LLaMA 3.3 via Groq API for intelligent document understanding and NLP-driven outputs. I'm experienced in Python, FastAPI, REST API development, and deploying AI into production environments with real users. For this R&D Tax Incentives platform specifically, I can contribute across: → Document ingestion and data extraction pipelines (NLP, OCR, LLM-based parsing) → AI models for comparing extracted findings against legislative criteria → Automated report generation with qualification likelihood scoring → FastAPI or Django backend with secure file upload and data handling → Clean technical documentation and stakeholder-ready outputs I'm available immediately, work remotely from Melbourne, Australia (UTC+10), and can engage across Hong Kong business hours. While I don't have direct HK regulatory experience, I'm a fast researcher — my track record includes independently picking up unfamiliar domain requirements and delivering within tight timelines. Portfolio: github.com/aryabhardwaj23 Contact: _Email not available. Sign in: https://www.twine.net/signup_

SURYANSH SHARMA

AI Engineer, Data Scientist, Developer, +1

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I'm an AI engineer with a Master of Artificial Intelligence (Monash University) and hands-on production experience building LLM-integrated applications and document-processing pipelines. Most relevant to this project: I've built end-to-end AI systems that ingest, analyse, and act on structured and unstructured data — including a live FastAPI + Vue.js application integrating Meta LLaMA 3.3 via Groq API for intelligent document understanding and NLP-driven outputs. I'm experienced in Python, FastAPI, REST API development, and deploying AI into production environments with real users. For this R&D Tax Incentives platform specifically, I can contribute across: → Document ingestion and data extraction pipelines (NLP, OCR, LLM-based parsing) → AI models for comparing extracted findings against legislative criteria → Automated report generation with qualification likelihood scoring → FastAPI or Django backend with secure file upload and data handling → Clean technical documentation and stakeholder-ready outputs I'm available immediately, work remotely from Melbourne, Australia (UTC+10), and can engage across Hong Kong business hours. While I don't have direct HK regulatory experience, I'm a fast researcher — my track record includes independently picking up unfamiliar domain requirements and delivering within tight timelines. Portfolio: github.com/aryabhardwaj23 Contact: _Email not available. Sign in: https://www.twine.net/signup_…I'm an AI engineer with a Master of Artificial Intelligence (Monash University) and hands-on production experience building LLM-integrated applications and document-processing pipelines. Most relevant to this project: I've built end-to-end AI systems that ingest, analyse, and act on structured and unstructured data — including a live FastAPI + Vue.js application integrating Meta LLaMA 3.3 via Groq API for intelligent document understanding and NLP-driven outputs. I'm experienced in Python, FastAPI, REST API development, and deploying AI into production environments with real users. For this R&D Tax Incentives platform specifically, I can contribute across: → Document ingestion and data extraction pipelines (NLP, OCR, LLM-based parsing) → AI models for comparing extracted findings against legislative criteria → Automated report generation with qualification likelihood scoring → FastAPI or Django backend with secure file upload and data handling → Clean technical documentation and stakeholder-ready outputs I'm available immediately, work remotely from Melbourne, Australia (UTC+10), and can engage across Hong Kong business hours. While I don't have direct HK regulatory experience, I'm a fast researcher — my track record includes independently picking up unfamiliar domain requirements and delivering within tight timelines. Portfolio: github.com/aryabhardwaj23 Contact: _Email not available. Sign in: https://www.twine.net/signup_

Available to hire

I’m an AI engineer with a Master of Artificial Intelligence (Monash University) and hands-on production experience building LLM-integrated applications and document-processing pipelines.

Most relevant to this project: I’ve built end-to-end AI systems that ingest, analyse, and act on structured and unstructured data — including a live FastAPI + Vue.js application integrating Meta LLaMA 3.3 via Groq API for intelligent document understanding and NLP-driven outputs. I’m experienced in Python, FastAPI, REST API development, and deploying AI into production environments with real users.

For this R&D Tax Incentives platform specifically, I can contribute across:

→ Document ingestion and data extraction pipelines (NLP, OCR, LLM-based parsing)
→ AI models for comparing extracted findings against legislative criteria
→ Automated report generation with qualification likelihood scoring
→ FastAPI or Django backend with secure file upload and data handling
→ Clean technical documentation and stakeholder-ready outputs

I’m available immediately, work remotely from Melbourne, Australia (UTC+10), and can engage across Hong Kong business hours. While I don’t have direct HK regulatory experience, I’m a fast researcher — my track record includes independently picking up unfamiliar domain requirements and delivering within tight timelines.

Portfolio: github.com/aryabhardwaj23
Contact: Email not available. Sign in: https://www.twine.net/signup

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Work Experience

AI Lead (internship) at Monash University

January 1, 2026 - June 1, 2026

Designed and deployed a nutrition-based recommendation experience for Australian caregivers (SDG 2 & 3). Implemented a content-based ML recommendation engine using KMeans, Random Forest, and cosine similarity on the AUSNUT 2023 dataset. Integrated AWS Rekognition (computer vision) and Meta LLaMA 3.3 (NLP) via Groq API into a live Vue.js + FastAPI web application. Led AI architecture, evaluation (F1, silhouette, bias analysis), GitHub workflow, and technical documentation for a 5-person cross-functional team.

ML/AI Research Assistant (contract) at Monash University

January 1, 2026 - Present

Implemented and validated biomedical AI research papers. Co-authored an applied research paper on identifying blood pressure using PPG signals through camera-based signals and related preprocessing/validation workflows.

Computer Vision Engineer (contract) at Next Gen Industries

January 1, 2026 - Present

Designed end-to-end computer vision pipelines including feature extraction, similarity scoring, and deployment using PyTorch and OpenCV. Built computer vision applications for civil inspection use-cases such as corrosion/crack detection, 3D point cloud mapping, and PPE detection using multiple hardware cameras and sensing systems (e.g., ZED, Helios, Triton, Oak-D, LiDARs, lasers). Integrated vision outputs with sensing hardware and performed spatial understanding/processing for industrial environments.

AI Lead at Monash University

January 1, 2026 - June 1, 2026

Designed and deployed a nutrition-based app for Australian caregivers and built a content-based recommendation engine using KMeans, Random Forest, and cosine similarity on the AUSNUT 2023 dataset. Integrated AWS Recognition computer vision and Meta Llama 3.3 NLP (via Groq API) into a live Vue.js + FastAPI application. Led AI architecture, model evaluation (F1, silhouette, bias analysis), GitHub workflow, and technical documentation for a 5-person cross-functional team (littlehelp.live).

ML/AI Research Assistant at Monash University

January 1, 2026 - Present

Implemented and validated AI research papers in biomedical AI. Co-authored an applied research paper related to identifying blood pressure using PPG signals through camera-based inputs. Worked on end-to-end experimentation, validation, and adaptation of research methods to biomedical signal/vision settings.

Computer Vision Engineer at Next Gen Industries

January 1, 2026 - Present

Designed end-to-end computer vision pipelines including feature extraction, similarity scoring, and deployment using PyTorch and OpenCV. Built computer vision applications for civil/industrial inspection such as corrosion and crack detection, PPE compliance monitoring, and object detection. Integrated multi-sensor data from hardware cameras and depth systems (e.g., ZED, Helios, Triton, Oak-D) and lidar/depth setups to support 3D mapping and spatial understanding. Focused on production-ready delivery: model evaluation, pipeline integration, and deployment in industrial environments.

Freelance AI Training Specialist at Outlier AI

October 1, 2024 - January 1, 2025

Trained and evaluated LLMs using RLHF-style data annotation workflows. Assessed model outputs for accuracy, relevance, and quality assurance. Focused on producing high-quality labeled training/evaluation data and conducting output quality checks.

Engineering Intern at BHEL / Perfect Engineering Corporation

June 1, 2020 - July 1, 2020

Designed engineering projects using AutoCAD and SolidWorks and analyzed industrial systems at a thermal power plant. Assisted in optimizing safety protocols and reviewing system/plant-level engineering considerations during internship work.