Job Description
Are you ready to define the trajectory of Artificial Intelligence in 2026 and beyond? Zai Future Systems is seeking a visionary Senior AI/LLM Engineer to architect the next generation of intelligent systems. We are building the infrastructure that will power the metaverse, autonomous agents, and next-gen data analytics.
In this role, you will not just maintain legacy models; you will pioneer new paradigms in Generative AI, Retrieval-Augmented Generation (RAG), and Reinforcement Learning from Human Feedback (RLHF). If you are passionate about pushing the boundaries of what is possible in 2026 and beyond, we want to meet you.
Why Join Us?
- Work with state-of-the-art infrastructure and proprietary datasets.
- Competitive compensation package including equity.
- Flexible remote-first culture with quarterly in-person team retreats.
- Access to the latest hardware for rapid prototyping.
Responsibilities
- Design and deploy scalable Large Language Model (LLM) infrastructure using PyTorch and TensorFlow.
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Lead the research and implementation of proprietary AI agents capable of complex reasoning.
- Optimize model inference latency and throughput for real-time, low-latency applications.
- Collaborate with product and engineering teams to integrate AI capabilities into consumer-facing applications.
- Establish best practices for MLOps, including CI/CD pipelines for model deployment.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- 5+ years of professional experience in machine learning engineering, specifically with Deep Learning.
- Expert proficiency in Python, PyTorch, and Hugging Face Transformers.
- Deep understanding of transformer architectures, attention mechanisms, and LLM fine-tuning techniques.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Proven track record of deploying high-availability ML systems in production environments.