Job Description
Are you ready to shape the trajectory of Artificial Intelligence in 2026?
Nexus Horizon AI is seeking a visionary Lead AI Engineer to join our elite engineering team. We are pioneering the next generation of Generative AI and Autonomous Systems, and we need a technical leader who is obsessed with scalability, innovation, and ethical AI development.
In this role, you will architect and implement state-of-the-art machine learning models that power our flagship platform. You will not just write code; you will define the architectural roadmap for our AI infrastructure, ensuring we remain at the bleeding edge of technology.
Why Join Us?
- Work on cutting-edge projects that define the future of the industry.
- Competitive compensation package with equity options.
- Flexible remote-first culture with state-of-the-art equipment.
- Opportunity to mentor junior talent and drive technical strategy.
Responsibilities
- Architect Scalable AI Systems: Design and deploy robust, scalable machine learning infrastructure capable of handling millions of daily interactions.
- Model Development: Lead the research and development of Large Language Models (LLMs) and deep learning algorithms.
- Optimization: Continuously improve model accuracy, latency, and efficiency through rigorous testing and A/B experiments.
- Technical Leadership: Mentor a team of data scientists and engineers, conducting code reviews and establishing best practices.
- Collaboration: Partner with product managers and stakeholders to translate complex business requirements into technical solutions.
- R&D: Stay ahead of the curve on emerging AI trends, including reinforcement learning and multi-modal AI.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a leadership or senior engineering role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Domain Knowledge: Deep understanding of Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Tools: Experience with cloud platforms (AWS/GCP/Azure), MLOps tools (Kubeflow, MLflow), and containerization (Docker/Kubernetes).
- Communication: Excellent ability to communicate complex technical concepts to non-technical audiences.