Job Description
We are on the forefront of technological evolution, building the intelligent systems that will define the year 2026 and beyond. Nexus Horizon AI is seeking a visionary Senior Next-Gen AI Engineer to lead our research division. In this role, you won't just implement existing models; you will architect the future of Generative AI, Large Language Models (LLMs), and autonomous decision-making systems. If you are passionate about pushing the boundaries of what is possible with artificial intelligence, this is your opportunity to shape the landscape of the future.
Why Join Us?
• Work with a world-class team of engineers, researchers, and data scientists.
• Access to cutting-edge compute resources and proprietary datasets.
• Competitive compensation package and equity opportunities.
• Flexible remote-first culture with a focus on innovation.
Responsibilities
- Model Architecture & Research: Design and implement novel neural network architectures, specifically focusing on next-generation Generative AI and LLM optimization for 2026 standards.
- Training Pipelines: Build scalable, high-performance training pipelines for large-scale datasets, utilizing distributed computing frameworks.
- System Optimization: Optimize model inference speeds and reduce latency to ensure real-time AI capabilities in production environments.
- Deployment & MLOps: Lead the deployment of AI models into production using containerization and cloud-native technologies (AWS/GCP).
- Technical Leadership: Mentor junior engineers and data scientists, conducting code reviews and architectural planning sessions.
- Ethical AI: Ensure all AI systems adhere to ethical guidelines, safety protocols, and fairness standards.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or AI research.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- LLM Expertise: Hands-on experience with fine-tuning LLMs (e.g., GPT, LLaMA, BERT) and RAG (Retrieval-Augmented Generation) architectures.
- Cloud Knowledge: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Proven ability to tackle complex mathematical and computational problems in high-pressure environments.