Job Description
Nexus Horizon AI is pioneering the next generation of generative intelligence. As we approach the pivotal year of 2026, we are looking for a visionary Senior AI/LLM Engineer to lead our research division. You will be at the forefront of building the foundational models that will define the next decade of human-computer interaction. If you are passionate about pushing the boundaries of Artificial General Intelligence (AGI) and want to solve the most complex challenges in Large Language Models (LLMs), we want to hear from you.
Why Join Us?
We are an elite team of researchers and engineers working on the 2026 AI Roadmap, focusing on scalability, efficiency, and ethical AI deployment. You will have the autonomy to experiment with cutting-edge architectures and the resources to turn theoretical breakthroughs into production-ready systems.
Responsibilities
- Architect & Develop: Lead the design and implementation of proprietary Large Language Models and multi-modal AI systems.
- Research & Innovation: Investigate novel neural network architectures and optimization techniques to improve model performance and reduce latency.
- RAG Systems: Design and deploy Retrieval-Augmented Generation pipelines to ensure accuracy and context-awareness in AI outputs.
- Productionize Models: Collaborate with MLOps engineers to transition research prototypes into scalable, high-availability production environments.
- Performance Tuning: Optimize inference pipelines for edge deployment and massive-scale cloud infrastructure.
- Team Leadership: Mentor junior engineers and conduct code reviews to maintain high technical standards across the AI research team.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or NLP.
- Core Tech: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Model Expertise: Deep understanding of Transformer architectures, BERT, GPT, and fine-tuning methodologies.
- Tools: Experience with Hugging Face, LangChain, and vector databases (e.g., Pinecone, Milvus).
- Deployment: Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).