Job Description
Join Zai Core Technologies as we pioneer the infrastructure for the enterprise of 2026 and beyond. We are seeking a visionary Senior AI Architect to lead our research and engineering efforts in Large Language Models (LLMs), Multi-modal systems, and autonomous agents. You will be at the forefront of defining how artificial intelligence interacts with complex enterprise workflows in a safe, scalable, and ethical manner.
As a key member of our elite technical team, you will bridge the gap between theoretical research and production-grade deployment, ensuring our AI solutions are not just cutting-edge, but robust and scalable for the future.
Responsibilities
- Lead AI Research & Development: Spearhead the design and implementation of state-of-the-art generative AI models, focusing on LLM optimization and fine-tuning strategies for 2026 readiness.
- Architect Scalable Systems: Build and maintain high-performance MLOps pipelines using modern cloud infrastructure (AWS/GCP) to ensure models scale efficiently.
- RAG & Integration: Design Retrieval-Augmented Generation (RAG) architectures to enhance model accuracy and reduce hallucinations in production environments.
- Cross-Functional Leadership: Collaborate closely with product managers, data scientists, and software engineers to translate business requirements into technical AI solutions.
- Mentorship: Mentor junior engineers and data scientists, fostering a culture of innovation, code excellence, and continuous learning within the AI department.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in building, deploying, and optimizing machine learning models at scale.
- Technical Proficiency: Deep expertise in Python, PyTorch, TensorFlow, and modern cloud platforms (AWS/GCP/Azure).
- AI Specialization: Demonstrated experience with Transformer architectures, LLM fine-tuning (LoRA, QLoRA), and prompt engineering.
- MLOps: Strong background in MLOps tools (Kubeflow, MLflow, Docker, Kubernetes) and CI/CD practices for machine learning.