Job Description
The Future is Now. Nexus Horizon is on a mission to define the technological landscape of the 2026 era. We are seeking a visionary Lead AI Architect to spearhead the development of our next-generation autonomous systems and neural infrastructure. If you are passionate about pushing the boundaries of what is possible and want to be at the forefront of the 2026 revolution, we want to meet you.
In this pivotal role, you will design scalable machine learning frameworks and ensure our products are ready for the rigorous demands of the upcoming year. You will work alongside a world-class team of engineers, data scientists, and product strategists to build the backbone of our future.
Why Join Us?
- Work on cutting-edge technology that defines the 2026 landscape.
- Competitive compensation package and equity opportunities.
- Flexible remote-first culture with a collaborative office environment.
- Access to top-tier hardware and research resources.
Ready to shape the future? Apply today.
Responsibilities
- Architect and implement high-performance machine learning pipelines designed for the 2026 production environment.
- Lead the design of scalable neural network architectures and optimize deep learning models for real-time inference.
- Define technical standards and best practices for AI model deployment, monitoring, and version control.
- Collaborate with cross-functional teams to translate business requirements into robust technical solutions.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Conduct rigorous code reviews and performance tuning to ensure system reliability and efficiency.
Qualifications
- Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- 7+ years of professional experience in machine learning engineering, with at least 3 years in a leadership or architect role.
- Expert proficiency in Python, PyTorch, or TensorFlow, with deep experience in C++ for performance-critical applications.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerization technologies.
- Proven track record of deploying scalable ML models to production environments.
- Experience with MLOps tools (MLflow, Kubeflow) and data pipeline technologies (Kafka, Airflow).
- Familiarity with the emerging regulatory and ethical standards for AI in 2026.