Job Description
Welcome to the future. Nexus Horizon is pioneering the next generation of artificial intelligence, and we are seeking a visionary Senior AI Architect to lead our 2026 roadmap. In this pivotal role, you will not just build systems; you will define the architectural standards for the next decade of human-machine collaboration. We are looking for a thought leader who thrives on ambiguity and is passionate about solving complex problems at the intersection of deep learning, quantum computing, and sustainable energy.
As we approach the 2026 horizon, our team is focused on creating scalable, ethical, and high-performance AI models. You will work closely with cross-functional squads to translate cutting-edge research into production-ready solutions that drive tangible business impact. Join us in shaping the intelligent infrastructure of tomorrow.
Responsibilities
- Design and architect scalable, high-performance AI systems and deep learning pipelines aligned with our 2026 strategic roadmap.
- Lead the technical strategy for implementing Generative AI and Large Language Models (LLMs) across enterprise applications.
- Mentor junior engineers and data scientists, fostering a culture of innovation, continuous learning, and technical excellence.
- Conduct research and prototyping for emerging technologies, including neuromorphic computing and edge AI.
- Collaborate with product managers and stakeholders to define technical requirements and ensure solution feasibility.
- Ensure AI systems are robust, secure, and compliant with global ethical AI standards and regulations.
- Drive optimization initiatives to improve model latency, accuracy, and resource efficiency.
Qualifications
- 10+ years of experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Master’s degree or PhD in Computer Science, Machine Learning, or a related technical field.
- Deep expertise in Python, TensorFlow, PyTorch, or similar deep learning frameworks.
- Proven track record of deploying large-scale machine learning models into production environments.
- Strong understanding of distributed systems, cloud architecture (AWS, GCP, or Azure), and MLOps practices.
- Excellent communication skills, with the ability to translate complex technical concepts for diverse audiences.
- Experience with AI ethics, bias mitigation, and responsible AI governance.