Job Description
We are at the forefront of the next technological revolution. Nexus Future Systems is seeking a visionary Lead AI Architect to define the architecture for our breakthrough AGI (Artificial General Intelligence) initiatives targeting the 2026 roadmap.
In this high-impact role, you will bridge the gap between theoretical machine learning research and scalable, production-grade infrastructure. You will lead a team of elite engineers in building the neural frameworks that will define the future of human-machine interaction. If you are passionate about pushing the boundaries of what is possible in artificial intelligence and want to shape the digital landscape of 2026 and beyond, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects with global impact.
- Competitive compensation package and equity options.
- Flexible remote-first culture with state-of-the-art equipment.
Responsibilities
- Architect and design scalable, high-performance AI infrastructure designed for the 2026 technological landscape.
- Lead the research and implementation of advanced deep learning models, including Transformer architectures and Quantum-inspired algorithms.
- Oversee the full machine learning lifecycle, from data ingestion and feature engineering to model deployment and monitoring.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate complex research into practical applications.
- Establish best practices for code quality, model governance, and ethical AI development.
- Mentor and guide junior architects and engineers to foster a culture of innovation and technical excellence.
Qualifications
- Master’s or PhD degree in Computer Science, Artificial Intelligence, or a related field (or equivalent extensive experience).
- Minimum of 8+ years of experience in software engineering, with at least 5 years specifically focused on AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, or similar ML frameworks.
- Proven track record of deploying production-level AI systems at scale.
- Strong understanding of large language models (LLMs), fine-tuning, and RAG (Retrieval-Augmented Generation) architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Excellent communication skills and the ability to articulate complex technical concepts to non-technical stakeholders.