Job Description
We are seeking a visionary Lead AI Architect to pioneer the next generation of intelligent systems. At OmniFuture Systems, we are not just building software; we are architecting the future of human-machine interaction for the year 2026 and beyond.
As a key member of our elite R&D division, you will be responsible for designing scalable, robust, and ethical AI frameworks that will power our global infrastructure. You will work at the intersection of deep learning, neural architecture search, and edge computing to deliver breakthrough solutions.
If you are passionate about pushing the boundaries of what is possible in artificial intelligence and thrive in a high-performance, innovative environment, we want to hear from you.
Responsibilities
- Architect and deploy state-of-the-art machine learning models, including Large Language Models (LLMs) and generative adversarial networks.
- Lead the research and development of novel algorithms to solve complex, unstructured problems in real-time.
- Establish best practices for MLOps, ensuring model scalability, reproducibility, and security.
- Mentor and guide a team of senior data scientists and engineers, fostering a culture of innovation and continuous learning.
- Collaborate cross-functionally with product managers, engineers, and stakeholders to translate business requirements into technical architectures.
- Define technical roadmaps for AI initiatives and evaluate emerging technologies to maintain a competitive edge.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 8 years of experience in machine learning, AI engineering, or a similar technical role.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying production-grade AI systems at scale.
- Strong understanding of cloud architectures (AWS, GCP, or Azure).
- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.
- Experience in AI ethics, bias mitigation, and responsible AI practices.