Job Description
Are you ready to define the technological landscape of 2026? At Nexus Horizon, we are building the AI infrastructure of tomorrow. We are seeking a visionary Senior AI Engineer to lead our Generative AI and Large Language Model (LLM) initiatives.
In this pivotal role, you will not only deploy state-of-the-art models but also shape the strategic roadmap for our 2026 product release. We are looking for a problem-solver who thrives in a fast-paced environment and is passionate about ethical AI development.
Why join us?
- Work on cutting-edge LLMs and multimodal AI systems.
- Competitive compensation package with equity options.
- Flexible remote-first policy with office hubs in San Francisco and New York.
Responsibilities
- Architect 2026-Ready AI Systems: Design, train, and deploy scalable machine learning models focused on long-term generative capabilities.
- Model Optimization: Fine-tune pre-trained models to improve performance, latency, and accuracy for enterprise applications.
- Research & Development: Stay ahead of the curve by implementing the latest research in Natural Language Processing (NLP) and Computer Vision.
- Collaboration: Partner with product managers and data scientists to translate business requirements into technical AI solutions.
- MLOps Implementation: Establish robust CI/CD pipelines for model deployment and monitoring.
- Code Review: Mentor junior engineers and ensure code quality across the AI engineering team.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Experience: Minimum of 5 years of professional experience in AI/ML engineering, with a proven track record of shipping production models.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Cloud Mastery: Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- LLM Expertise: Deep understanding of transformer architectures, prompt engineering, and fine-tuning methodologies.
- Problem Solving: Ability to handle large-scale data challenges and optimize model inference in real-world scenarios.