Job Description
Are you ready to define the technology landscape of 2026?
Quantum Horizon Systems is seeking a visionary Senior AI Architect to lead our next-generation generative AI and neural interface research. You won't just build models; you will architect the foundational infrastructure required for the next leap in human-machine interaction.
In this role, you will oversee the end-to-end lifecycle of our AI initiatives, ensuring our solutions are scalable, ethical, and cutting-edge. We are looking for a pioneer who thrives in ambiguity and is driven by the prospect of transforming industries through advanced artificial intelligence.
Quantum Horizon Systems is seeking a visionary Senior AI Architect to lead our next-generation generative AI and neural interface research. You won't just build models; you will architect the foundational infrastructure required for the next leap in human-machine interaction.
In this role, you will oversee the end-to-end lifecycle of our AI initiatives, ensuring our solutions are scalable, ethical, and cutting-edge. We are looking for a pioneer who thrives in ambiguity and is driven by the prospect of transforming industries through advanced artificial intelligence.
Responsibilities
- Architect and deploy scalable, high-performance AI models designed for the 2026 horizon.
- Lead research initiatives in Large Language Models (LLMs) and autonomous agents.
- Establish best practices for MLOps and ethical AI governance frameworks.
- Collaborate with product and engineering teams to translate complex AI capabilities into user-centric experiences.
- Mentor a team of top-tier data scientists and engineers.
- Define technical roadmaps for AI integration across enterprise platforms.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related field.
- 7+ years of experience in machine learning engineering, preferably in a startup or high-growth environment.
- Deep expertise in Python, PyTorch, and TensorFlow.
- Proven track record of delivering production-grade AI systems to market.
- Strong understanding of LLM fine-tuning and RAG (Retrieval-Augmented Generation) pipelines.
- Experience with cloud infrastructure (AWS, GCP) and containerization (Docker, Kubernetes).