Job Description
Shape the Future of Intelligence. Nexus Horizon is pioneering the technological advancements expected in the year 2026 and beyond. We are seeking a visionary Senior Generative AI Architect to lead the design and implementation of next-generation Large Language Models (LLMs) and multimodal AI systems.
In this high-impact role, you will bridge the gap between theoretical research and production-grade engineering, ensuring our AI solutions are scalable, secure, and ethically aligned. If you are passionate about the frontier of AI and want to build the infrastructure that powers the future, we want to meet you.
Responsibilities
- Architect LLM Pipelines: Design and optimize large-scale machine learning pipelines for training, fine-tuning, and deploying state-of-the-art generative models.
- Model Optimization: Implement quantization, pruning, and inference acceleration techniques to ensure models run efficiently on edge devices and cloud infrastructure.
- R&D Leadership: Stay ahead of the curve by researching emerging architectures (e.g., Mixture of Experts, Retrieval-Augmented Generation) and integrating them into our product suite.
- Collaboration: Work closely with data scientists, product managers, and security teams to define technical requirements and mitigate risks.
- Mentorship: Guide a team of junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of experience in machine learning engineering, with a strong focus on Natural Language Processing (NLP) and Generative AI.
- Technical Skills: Proficiency in PyTorch, TensorFlow, or JAX. Deep understanding of transformer architectures and attention mechanisms.
- Programming: Expert-level Python and C++ skills; experience with distributed computing frameworks (e.g., Apache Spark, Ray).
- Problem Solving: Demonstrated ability to solve complex system-level problems and optimize model performance in real-world scenarios.