Job Description
Join Nexus Horizon Inc., a pioneer in next-generation artificial intelligence, as we architect the systems that will define the technological landscape of 2026 and beyond. We are looking for a visionary Senior Generative AI Engineer to lead our efforts in building scalable, efficient, and ethically sound Large Language Models (LLMs).
In this role, you will bridge the gap between cutting-edge research and production-grade engineering. You will be instrumental in fine-tuning foundation models, optimizing inference latency, and designing architectures that push the boundaries of what is possible in generative AI.
Why Join Us?
- Future-Proof Your Career: Work on core technologies expected to dominate the market by 2026.
- Unlimited Growth: Competitive equity package and clear path to technical leadership.
- Top-Tier Talent: Collaborate with a team of world-class researchers and engineers.
If you are passionate about the future of AI and want to build systems that think, reason, and create, we want to hear from you.
Responsibilities
- Design, train, and fine-tune proprietary and open-source Large Language Models (LLMs) for diverse enterprise applications.
- Implement MLOps pipelines to ensure the scalability, reproducibility, and monitoring of AI models in production environments.
- Optimize model inference speed and reduce token costs using techniques such as quantization, pruning, and distillation.
- Conduct rigorous research to integrate the latest advancements in Natural Language Processing (NLP) into our product suite.
- Collaborate with product and engineering teams to define AI requirements and translate them into technical specifications.
- Ensure AI safety, bias mitigation, and compliance with regulatory standards.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field (or equivalent practical experience).
- 5+ years of professional experience in Machine Learning, Deep Learning, or NLP.
- Expert proficiency in Python and major deep learning frameworks (PyTorch, TensorFlow, or JAX).
- Strong experience with LLM architectures (Transformers, GPT, BERT) and fine-tuning methods (PEFT, LoRA, QLoRA).
- Experience deploying AI models via cloud platforms (AWS, GCP, or Azure) using containerization technologies (Docker, Kubernetes).
- Proven track record of publishing in top-tier AI conferences (NeurIPS, ICML, ACL) or open-sourcing significant projects.