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I am a full-stack AI engineer who designs and delivers end-to-end AI systems, from data pipelines and model development to evaluation and deployment. I excel at bridging experimentation and production to build scalable, reliable, and practical AI solutions across research and applied engineering, including video understanding, motion data processing, sign language translation/generation, and LLM-powered product development. I graduated from Monash University with a Master of Science in Artificial Intelligence (Research Pathway) with Distinction, and I have hands-on experience through internships at Monash Assistive Tech and the Chinese Academy of Sciences’ Institute of Computing Technology. I enjoy turning research ideas into reusable engineering pipelines and collaborating across disciplines to ship real-world AI.…I am a full-stack AI engineer who designs and delivers end-to-end AI systems, from data pipelines and model development to evaluation and deployment. I excel at bridging experimentation and production to build scalable, reliable, and practical AI solutions across research and applied engineering, including video understanding, motion data processing, sign language translation/generation, and LLM-powered product development. I graduated from Monash University with a Master of Science in Artificial Intelligence (Research Pathway) with Distinction, and I have hands-on experience through internships at Monash Assistive Tech and the Chinese Academy of Sciences’ Institute of Computing Technology. I enjoy turning research ideas into reusable engineering pipelines and collaborating across disciplines to ship real-world AI.

Singyu Muk

AI Engineer, Data Scientist, Full Stack Developer, +3

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I am a full-stack AI engineer who designs and delivers end-to-end AI systems, from data pipelines and model development to evaluation and deployment. I excel at bridging experimentation and production to build scalable, reliable, and practical AI solutions across research and applied engineering, including video understanding, motion data processing, sign language translation/generation, and LLM-powered product development. I graduated from Monash University with a Master of Science in Artificial Intelligence (Research Pathway) with Distinction, and I have hands-on experience through internships at Monash Assistive Tech and the Chinese Academy of Sciences’ Institute of Computing Technology. I enjoy turning research ideas into reusable engineering pipelines and collaborating across disciplines to ship real-world AI.…I am a full-stack AI engineer who designs and delivers end-to-end AI systems, from data pipelines and model development to evaluation and deployment. I excel at bridging experimentation and production to build scalable, reliable, and practical AI solutions across research and applied engineering, including video understanding, motion data processing, sign language translation/generation, and LLM-powered product development. I graduated from Monash University with a Master of Science in Artificial Intelligence (Research Pathway) with Distinction, and I have hands-on experience through internships at Monash Assistive Tech and the Chinese Academy of Sciences’ Institute of Computing Technology. I enjoy turning research ideas into reusable engineering pipelines and collaborating across disciplines to ship real-world AI.

Available to hire

I am a full-stack AI engineer who designs and delivers end-to-end AI systems, from data pipelines and model development to evaluation and deployment. I excel at bridging experimentation and production to build scalable, reliable, and practical AI solutions across research and applied engineering, including video understanding, motion data processing, sign language translation/generation, and LLM-powered product development.

I graduated from Monash University with a Master of Science in Artificial Intelligence (Research Pathway) with Distinction, and I have hands-on experience through internships at Monash Assistive Tech and the Chinese Academy of Sciences’ Institute of Computing Technology. I enjoy turning research ideas into reusable engineering pipelines and collaborating across disciplines to ship real-world AI.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Intership at Monash Assistive Tech

March 1, 2025 - March 1, 2026

•Develop sign language AI models for translation, recognition, and generation, with a focus on motion representation learning and practical model performance. Achieved DTW_MPJPE_PA hand reconstruction metrics on How2Sign of 0.631 (left hand) and 0.750 (right hand) for the tokenizer setting, compared with a reported SOTA reference of 2.35 (both hands), representing approximately 70% lower hand reconstruction error. • Built a multi-view video to integrated 3D pose extraction pipeline using motion extraction models, wavelet based denoising, non-sign-motion filtering, and multi-view data fusion, producing smoother and higher quality 3D pose outputs for sign generation workflows. • Applied few-shot LLM prompting to text-to-gloss translation as an alternative data generation/translation strategy in sign language translation research workflows.

Internship at Digital Content Synthesis and Forgery Detection Laboratory

November 28, 2024 - March 1, 2025

• Built and maintained a video-to-pose extraction workflow for motion data preparation, including action/motion recognition-based skeleton extraction from video sources. • Performed pose data denoising, correction, and quality refinement to improve usability of extracted motion sequences for downstream model training. • Contributed to preprocessing and validation steps for motion datasets, improving consistency and reducing noisy or invalid samples in the training pipeline.

AI Internship (Sign Language Generation) at Monash Assistive Tech, Monash University

March 1, 2025 - Present

Develop sign language AI models for translation, recognition, and generation. Designed and implemented a high-performance 3D human motion dual-stream VAE/tokenizer for sign motion modeling, achieving substantially lower hand motion reconstruction error on How2Sign compared with a recent state-of-the-art benchmark. Achieved DTW_MPJPE_PA hand reconstruction metrics on How2Sign of 0.631 (left hand) and 0.750 (right hand) for the tokenizer setting, vs ~2.35 for the SOTA reference. Improved continuous representation reconstruction to 0.435 (left) and 0.560 (right). Built a multi-view video-to-integrated 3D pose extraction pipeline with motion extraction models, wavelet-denoising, non-sign-motion filtering, and multi-view data fusion. Applied few-shot LLM prompting to text-to-gloss translation. Conducted systematic training, evaluation, and error analysis, bridging research experimentation with reusable engineering pipelines.

AI Internship (Video Generation & Motion Data Pipeline) at Digital Content Synthesis and Forgery Detection Laboratory, Institute of Computing Technology, Chinese Academy of Sciences

November 1, 2024 - February 1, 2025

Built and maintained a video-to-pose extraction workflow for motion data preparation, including action/motion recognition-based skeleton extraction from video sources. Performed pose data denoising, correction, and quality refinement to improve usability of extracted motion sequences for downstream model training. Contributed to preprocessing and validation steps for motion datasets, improving consistency and reducing noisy or invalid samples. Supported applied research workflows in video understanding and motion data processing for generation-oriented modeling tasks.