Job responsibilities:

Develop machine learning computer vision applications in object detection, classification, segmentation and recognition for gemstones

Develop LLM-applications specific to gemstone domain leveraging on latest frameworks

Conduct User requirements, scope the AI/computer-vision project and prepare documents to define nature of dataset required for machine-learning model training 

Work collaboratively with an entrepreneurial team of IOT-product engineers, machine learning & data scientist and software engineers, both in-house or Vendors, to successfully develop and deploy a cutting-edge solution

Develop a comprehensive MLOps environment to manage the entire lifecycle and pipeline of machine learning projects, as well as maintain a well-documented code repository

Conduct research and product development projects using internal gatekeeping process, prepare project and user requirements specifications, execute experiments and investigations, document and report findings, communicate to key stakeholders, and handover to downstream teams

Execute the necessary data preparation & pre-processing, AI-model building, testing, validation and deployment to ensure reliable and scalable AI solution

Requirements:

Bachelor’s or master’s degree in computer science, data science, artificial intelligence or related engineering fields.

Minimum 3 to 5 years of proven experience in developing ML/DL architectures to solve real-world computer vision problems

Strong proficiency in Python with hands-on experience in mainstream deep learning frameworks (PyTorch and TensorFlow) and their associated computer vision libraries (e.g., OpenCV, torchvision, timm, Hugging Face Transformers)

Experience in computer vision with OpenCV and modern deep learning architectures, with a strong emphasis on Vision Transformer (ViT)-based models (e.g., DETR, DINOv2, SAM) and vision–language foundation models such as CLIP and its variants (SigLIP, BLIP); working knowledge of contrastive learning, self-supervised pre-training, and few-shot / zero-shot adaptation for downstream tasks including detection, segmentation, classification, and retrieval

In-depth understanding of digital image processing, image transformation techniques, real-time image analytics and feature extraction in computer vision

Experience in large-scale image data processing and architecting of data pipelines

Possess data storytelling, information visualisation and technical writing skills

Excellent research and problem-solving abilities, whilst maintaining curiosity about new things and enthusiasm to learn and share latest methods/techniques within the team

Hands-on experience with cloud platforms (AWS, GCP) and modern backend/serving stacks (FastAPI, Redis, Docker) for end-to-end AI model development, deployment, and scalable inference

Hands-on proficiency with modern AI coding agents (e.g., Claude Code, Cursor, GitHub Copilot), including their extensibility features such as Skills, Plugins, sub-agents and MCP integrations; strong prompt-engineering skills and the ability to leverage AI-assisted development workflows productively in day-to-day engineering tasks

Command good communication and collaboration skills, with a strong sense of responsibility, integrity and reliability