AI Engineer

Role Overview We are seeking a versatile AI Engineer to lead the development of high-quality, audio-driven digital avatars. This role combines cutting-edge Generative AI with foundational Machine Learning to create responsive, identity-consistent virtual humans. You will bridge the gap between "brain" and "body" by integrating RAG-based agents with multimodal synthesis models (ViT/VLM) to build avatars that don't just look real—they interact intelligently.

Core Responsibilities Multimodal Synthesis: Develop SOTA audio-to-video pipelines using Vision Transformers (ViT) and VLMs to drive lip-sync, micro-expressions, and head poses. Intelligent Interaction: Architect RAG (Retrieval-Augmented Generation) systems using Lang. Chain and AI Agents to provide avatars with a searchable knowledge base and autonomous reasoning capabilities.

Customized Avatar Generation: Build person-specific fine-tuning workflows ( LoRA , Adapters ) to ensure 1:1 identity preservation from minimal reference footage. Hybrid Modeling: Apply a mix of Deep Learning (CNNs for texture, RNN/LSTM for temporal audio sequences) and Classical ML (XGBoost/Random Forest for metadata classification or signal gating).

End-to-End Optimization: Own the pipeline from raw audio/text input to real-time rendered output, ensuring low-latency performance on GPU clusters. Required Technical Stack Generative AI & Agents: Frameworks: Mastery of Lang. Chain or Llama. Index for building RAG pipelines. Agents: Experience deploying autonomous agents to handle multi-step reasoning tasks.

Computer Vision & Multimodal: Architectures: Deep expertise in ViT (feature encoding) and VLM (CLIP/BLIP for alignment). Deep Learning: Hands-on experience with CNNs (spatial features), RNNs/LSTMs (temporal audio-visual sync), and GANs/Diffusion . Core Machine Learning: Algorithms: Proficiency in Random Forest , XGBoost , and SVMs for auxiliary data tasks (e.

g., emotion classification or quality gating). Frameworks: Py. Torch (primary), Tensor. Flow , and Scikit-learn . Data & Infrastructure: Vector DBs: Experience with Pinecone, Milvus, or Weaviate for RAG storage. Tools: FFmpeg for video processing and NVIDIA Deep. Stream for deployment.

Qualifications

5+ years in Data Science with a focus on Multimodal ML or Digital Humans.

Education

Master’s or PhD in CS, AI, or a related quantitative field. Problem Solving: Proven ability to solve the "uncanny valley" through superior temporal consistency and identity-aware fine-tuning. Location: Guangzhou (DTC) Job: Data Technology Schedule: Regular Employee Status: Full time