Job Description
Join Nexus Horizon and Architect the Future.
We are a cutting-edge technology firm pioneering the AI infrastructure for the year 2026 and beyond. We are looking for a visionary Senior AI Architect to lead our research and development division. If you are passionate about pushing the boundaries of generative models, quantum-ready algorithms, and ethical AI, this is your opportunity to define the standard for intelligent systems.
What You Will Do:
As a key player in our 2026 roadmap, you will design scalable, high-performance AI systems that power next-generation applications. You will bridge the gap between theoretical research and production deployment, ensuring our solutions are robust, efficient, and transformative.
Why Nexus Horizon?
• Impactful Work: Directly influence the trajectory of artificial intelligence technology.
• Elite Team: Collaborate with world-class engineers, data scientists, and futurists.
• Competitive Compensation: We offer a top-tier salary and equity package.
Responsibilities
- Lead the architecture and design of complex deep learning models and neural networks tailored for 2026 computing paradigms.
- Oversee the end-to-end lifecycle of AI projects, from research and prototyping to production deployment and maintenance.
- Optimize existing models for speed, accuracy, and resource efficiency on distributed cloud architectures.
- Collaborate with product managers and engineering teams to define technical requirements and product roadmaps.
- Mentor junior data scientists and engineers, fostering a culture of continuous learning and innovation.
- Stay ahead of industry trends, evaluating new technologies such as edge computing and neuromorphic hardware.
Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related field with a focus on AI.
- Minimum of 5-7 years of professional experience in building and deploying large-scale machine learning systems.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of NLP, Computer Vision, or Reinforcement Learning principles.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to diverse stakeholders.