Offer summary

Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related field., Experience with generative models and computer vision techniques., Proficiency in advanced prompt engineering and visual conditioning mechanisms., Familiarity with open-source models like Stable Diffusion and VideoCrafter..

Key responsibilities:

Collaborate with AI engineers, product managers, and content creators to develop visual AI features.

Fine-tune diffusion-based models using techniques like Dreambooth and LoRA.

Enhance existing image and video generation pipelines with state-of-the-art techniques.

Contribute to tools and libraries for dataset preparation and quality control.

Job description

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio. We are currently seeking an AI Vision Engineer to join one of our clients' teams. If you're looking for an exciting opportunity to grow in an innovative environment, this could be the perfect fit for you.

As an AI Vision Engineer, you will help drive the development of visual intelligence capabilities that power conversational AI experiences. You’ll work hands-on with generative models and computer vision techniques to evolve the image and video stack, improving quality, consistency, and user engagement. This is a highly applied role, focused on building and shipping production-grade features that enhance the creative potential of our platform.

Key Responsibilities

Collaborate closely with AI engineers, product managers, and content creators to define and execute on visual AI features
Fine-tune diffusion-based models by embedding new concepts using techniques like Dreambooth, LoRA, or textual inversion
Improve and extend our existing image and video generation pipelines using state-of-the-art computer vision and generative modeling techniques
Implement advanced prompt engineering strategies and visual conditioning mechanisms to ensure high-quality output across diverse user scenarios.
Evaluate, adapt, and integrate relevant open-source models (e.g. Stable Diffusion, VideoCrafter, ModelScope, Hunyuan)
Contribute to shared tools and internal libraries that support dataset preparation, experimentation, and quality control