Generative AI Engineer

Responsibilities

• Research & Literature Survey
  - Investigate and analyze the latest research papers in Generative AI, LLMs, Computer Vision, Natural Language Processing, etc.
  - Monitor industry trends and assess applicability to our products
  - Leverage academic presentation experience for technology evaluation from scholarly perspectives
• Model Development & Implementation
  - Design and develop state-of-the-art Generative AI models (GPT, Stable Diffusion, Custom Transformers, etc.)
  - Customize and structurally transform existing large language models
  - Implement using frameworks like PyTorch, TensorFlow, Hugging Face Transformers
  - Develop models across multiple modalities (image generation, speech recognition, text generation, etc.)
• Performance Optimization
  - Optimize inference pipelines (reduce latency, improve throughput)
  - Apply optimization techniques (quantization, distillation, pruning)
  - Implement acceleration using GPU/TPU hardware
  - Ensure scalability and robustness for production environments
• Evaluation & Verification
  - Build model performance evaluation frameworks
  - Analyze and improve issues (hallucinations, bias, output quality)
  - Conduct benchmark testing and drive continuous improvement cycles
• Product Integration
  - Implement developed models as APIs and documentation for the product team use
  - Implement integration technologies (Vector DB, embeddings, RAG)
  - Build and operate AI systems on cloud platforms (GCP)

Required Qualifications

• Academic & Foundational Knowledge
  - Master's or Ph.D. degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields
  - Deep knowledge/experience in the Generative AI domain
• Technical Skills
  - Advanced Python programming: Practical experience with deep learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
  - Deep learning fundamentals: Understanding of neural networks, Transformers, and attention mechanisms
  - Generative AI model implementation experience: Development/implementation experience with at least one Generative AI model (LLM, diffusion models, GANs, etc.)
• Research & Implementation Experience
  - Design and implementation experience of algorithms based on the latest research papers in Computer Vision or Natural Language Processing
  - Research project experience with independent algorithm development and publication in academic conferences/journals
  - Experience applying research outcomes to actual products and achieving performance improvements
• Problem-Solving Ability
  - Experience analyzing specific technical challenges in real projects and proposing/implementing concrete solutions
  - Ability to address complex technical problems with creative approaches