Job Description
We are seeking a visionary Lead AI Architect to spearhead the technological evolution of Nexus Horizon Systems. As we prepare to redefine the digital landscape for the year 2026, you will be responsible for designing scalable, autonomous AI ecosystems that integrate seamlessly with emerging quantum interfaces.
In this high-impact role, you will bridge the gap between theoretical machine learning research and production-grade infrastructure. You will lead a team of elite engineers to build the neural foundations of our next-generation products, ensuring our platform remains at the cutting edge of artificial general intelligence.
Why Join Us?
- Work on the 2026 Horizon Project – defining the future of human-computer interaction.
- Competitive equity package and top-tier health benefits.
- Flexible remote-first culture with quarterly all-hands innovation summits.
- Access to state-of-the-art compute resources and proprietary datasets.
Responsibilities
- Architect and deploy advanced neural networks designed for the 2026 operational timeline, focusing on low-latency, high-throughput inference.
- Lead the technical roadmap for our proprietary LLM (Large Language Model) evolution, ensuring ethical AI governance and safety alignment.
- Collaborate with cross-functional product teams to translate complex 2026 strategic goals into technical specifications.
- Optimize existing infrastructure for edge computing environments and next-gen hardware accelerators.
- Establish best practices for model monitoring, version control, and CI/CD pipelines for AI models.
- Mentor senior engineers and data scientists, fostering a culture of continuous learning and innovation.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on Artificial Intelligence.
- Minimum of 8 years of experience in software engineering, with at least 5 years dedicated to Machine Learning and Deep Learning architecture.
- Expert proficiency in Python, C++, and TensorFlow/PyTorch.
- Proven track record of deploying large-scale AI systems in production environments.
- Strong understanding of distributed systems, cloud architecture (AWS/Azure/GCP), and Kubernetes.
- Experience with reinforcement learning, federated learning, or generative adversarial networks is highly preferred.