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Job Description:
The role is responsible for driving AI innovation by conceptualizing and developing Proof-of-Concept solutions using modern AI technologies. He/She will partners closely with business users to co-define requirements, selects appropriate technology stacks, and iteratively showcases prototype progress for continuous feedback. The role also ensures smooth solution transition to BAU teams by supporting adoption, knowledge transfer, and alignment with enterprise standards.
Major accountabilities:
Design and develop AI-powered proof-of-concept prototypes that demonstrate innovative solutions and validate emerging technologies for real-world business challenges based on three major criteria, namely: accuracy, performance and cost
Collaborate closely with business stakeholders to gather requirements, understand application needs, and translate them into technical specifications and prototype features through an iterative approach with small baby step enhancement
Evaluate and recommend appropriate technology stacks by assessing scalability, performance, and alignment with organizational goals, ensuring optimal implementation of AI solutions.
Present prototypes in iterative cycles to stakeholders, incorporating feedback to refine functionality, improve usability, and enhance overall solution effectiveness
Explain to Risk, Compliance and Infosec about how the solution design is supposed to work and what risk containment measures have been taken to minimize possible technology risk. For example through "human in the loop supervision" and "human outside the loop" as guardrails.
Facilitate smooth technology transfer by working with operations teams to transition prototypes into production-ready systems, ensuring knowledge sharing and adoption. Provide clear documentation on coding as well as functionalities through written text and/or Confluence page.
Maintain comprehensive technical documentation and version-controlled repositories to support ongoing development, knowledge management, and future enhancements.
Requirement:
5 years of app/dev experience for commercial organisation
Education in engineering background; IT related University degree is not mandatory
Fluent in spoken English and Chinese
Strong in advanced mathematics. (AI relies on a foundation of linear algebra for data representation (vectors/matrices), calculus for model optimization (gradients/derivatives), and probability/statistics to manage uncertainty).
Conviction in the necessity to adhere to business requirements
Hands-on skillsets on Repository Management (Github, GitLab, Bitbucket), Software Tools (Terminal, Powershell, Pycharm, VS Code, IntelliJ), Business Process Management (Confluence, Jira, Zoho, Asana), Computer Vision (CNN, IP/Industrial Cameras, RTSP, RTMP, MJPEG streaming protocols)
Familiar skillsets on: • LLM Workflow Orchestration: Multi-step LLM workflows using Dify and custom orchestration: user-intent understanding, planning sequences of calls, and coordinating tools/APIs end-to-end (workflow patterns). • LLM Development and Tools: Axolotl Training Suite, Dify, xInference, vLLM, HuggingFace, Low Rank Adaption, Data Set curation, text embedding, Re-Rankers, Langchain, Langgraph, Gemini, Qwen, DeepSeek, Llama, ChatGPT family of LLM, SLM and code builder models • Python: Pycharm, Gradio, OpenCV, YOLO, DLIB, LangChain, Pytorch, Tensorflow, CUDA • OCR Models: Azure Document Intelligent, Google Vision API, Qwen OCR, Paddle, DOTS.OCR, • Containers/VM/Cloud Services:Docker, VMWare, Azure VM, VirtualBox • QA/QC: jQuery, Postman, GenAI Judges, Langfuse • Back-end related: MySQL, JSON, PostgreSQL, Image AI:Stable Diffusion, Stable Dreamfusion, Low-rank Adaptation, ControlNet, Hyper networks
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