Job Description
Join the Vanguard of AI Innovation (Project 2026)
Nexus Horizon Systems is on a mission to define the artificial intelligence landscape of the future. We are currently recruiting for our elite Project 2026 team—a high-performance research division dedicated to developing next-generation Large Language Models (LLMs) and autonomous agents. We are looking for a visionary Senior Generative AI Engineer to lead the architecture and deployment of systems that will redefine human-machine interaction.
As part of Project 2026, you will work at the intersection of theoretical research and scalable engineering. You will have the autonomy to experiment with cutting-edge architectures while ensuring robust, production-grade performance. If you are passionate about the future of AI and want to build the foundational models of tomorrow, we want to meet you.
Responsibilities
- Lead R&D for Next-Gen Models: Architect and fine-tune state-of-the-art generative models, specifically focusing on reasoning, multimodal capabilities, and reduced hallucination rates.
- Optimize Inference Pipelines: Design and implement high-performance inference systems, utilizing techniques such as quantization, distillation, and hardware acceleration (TPU/GPU) to minimize latency and cost.
- Collaborate with Cross-Functional Teams: Partner with product managers, researchers, and frontend engineers to translate complex AI capabilities into intuitive user experiences.
- Ensure Ethical AI Standards: Implement rigorous safety and alignment protocols to ensure model outputs are fair, unbiased, and adhere to regulatory standards.
- Mentorship & Code Review: Guide junior engineers and researchers in best practices for machine learning operations (MLOps) and deep learning engineering.
- Research & Publication: Contribute to internal whitepapers and potentially external academic research to establish Nexus Horizon as a thought leader in the AI community.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field.
- Technical Expertise: Deep understanding of Deep Learning architectures (Transformers, GNNs, RNNs) and frameworks such as PyTorch, TensorFlow, or JAX.
- Experience: 5+ years of professional experience in building, deploying, and optimizing large-scale machine learning models.
- Programming: Proficiency in Python, C++, or Rust; experience with distributed computing systems (Kubernetes, Ray, Spark).
- Model Fine-tuning: Extensive experience with LLM fine-tuning methodologies (PEFT, LoRA, SFT) and model evaluation frameworks (Hugging Face, EleutherAI).
- Problem Solving: Strong analytical skills with a proven track record of solving complex engineering challenges in high-pressure environments.