Job Description
The Future of Intelligence is Here.
Nexus Horizon AI is pioneering the next generation of artificial intelligence. We are looking for a visionary AI Architect to design and implement the neural architectures that will define the landscape of 2026 and beyond. If you are passionate about pushing the boundaries of generative models, autonomous agents, and ethical AI, this is your stage.
You will work at the intersection of theoretical research and scalable engineering, building systems that not only think but understand context, nuance, and future intent. Join a team of elite engineers and researchers committed to shaping the digital reality of tomorrow.
Responsibilities
- Architect Next-Gen Neural Networks: Design and deploy proprietary Large Language Models (LLMs) and multimodal AI systems capable of complex reasoning and autonomous decision-making.
- MLOps & Infrastructure: Build robust, high-throughput inference pipelines and training clusters optimized for cost-efficiency and low latency in cloud and edge environments.
- Model Evaluation & Alignment: Implement rigorous testing frameworks to ensure model safety, bias mitigation, and alignment with human values.
- Research Integration: Translate cutting-edge academic research into production-ready code, bridging the gap between theory and practical application.
- System Scalability: Architect systems capable of handling exabytes of data, ensuring seamless scaling as our user base expands globally.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related field. (PhD preferred for research-focused roles).
- Deep expertise in Python, PyTorch, or TensorFlow. Experience with distributed training frameworks is a plus.
- Proven track record of deploying large-scale ML models. Experience with Hugging Face, vLLM, or similar inference engines.
- Strong understanding of Deep Learning architectures. including Transformers, Diffusion models, and Reinforcement Learning from Human Feedback (RLHF).
- Familiarity with MLOps tools. such as MLflow, Kubeflow, or custom CI/CD pipelines for machine learning.