Job Description
Join Nexus Horizon Labs as we pioneer the 2026 AI Paradigm. We are not just building software; we are architecting the cognitive infrastructure for the next decade. We are looking for a visionary Senior AI Architect to lead the development of next-generation Autonomous Agents and Generative Multimodal Models. If you are obsessed with pushing the boundaries of Large Language Models (LLMs), optimizing inference for edge devices, and solving complex reasoning problems, this is your opportunity to define the future of technology.
In this role, you will bridge the gap between cutting-edge research and production-grade systems. You will work in a high-performance environment with top-tier talent to deploy AI solutions that are safe, scalable, and transformative.
Responsibilities
- Lead Model Architecture: Design and implement state-of-the-art transformer architectures and neural network models tailored for the 2026 era of AI.
- Optimize Inference: Engineer efficient model serving pipelines using techniques like quantization, distillation, and caching to reduce latency and cost.
- Autonomous Agents: Build the logic and tool-use capabilities for AI agents capable of complex, multi-step reasoning and self-correction.
- Data Pipelines: Construct robust MLOps pipelines for data ingestion, preprocessing, and active learning to continuously improve model performance.
- Cross-Functional Collaboration: Partner with product managers, researchers, and engineers to translate technical requirements into scalable AI solutions.
- Ethical AI: Implement safety measures and bias mitigation strategies to ensure responsible deployment of AI systems.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Core Tech: Deep expertise in Python, PyTorch, or TensorFlow.
- Modeling: Proven experience training and fine-tuning Large Language Models (LLMs) and diffusion models.
- Experience: 5+ years of experience in machine learning engineering, software engineering, or research.
- System Design: Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Problem Solving: Ability to tackle complex, ambiguous problems with creative algorithmic solutions.