Job Description
We are building the infrastructure for the next decade. Nexus 2026 Technologies is seeking a visionary Senior AI Architect to lead our breakthrough initiatives in Generative AI, Quantum-Ready Algorithms, and Autonomous Systems.
In this pivotal role, you won't just use existing tools; you will architect the future of intelligent computing. You will define the architectural patterns for scalable, ethical, and high-performance AI systems that will define the 2026 era and beyond.
Why Join Us?
- Work on mission-critical projects that redefine industry standards.
- Competitive equity package and top-tier compensation.
- Access to cutting-edge hardware and research facilities.
- A culture of innovation that prioritizes impact over process.
If you are a technical leader ready to push the boundaries of what is possible in artificial intelligence, we want to hear from you.
Responsibilities
- Design and implement robust, scalable AI architectures capable of handling petabyte-scale data and complex neural networks.
- Lead the research and development of next-generation Generative AI models, including LLM fine-tuning and deployment strategies.
- Oversee the integration of quantum computing principles into classical machine learning workflows.
- Establish best practices for AI governance, including bias mitigation, transparency, and ethical AI compliance.
- Mentor a team of brilliant engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Collaborate cross-functionally with product, engineering, and design teams to translate complex technical requirements into user-centric solutions.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- 10+ years of experience in software engineering, with at least 5 years in specialized AI/ML architecture.
- Deep expertise in Python, PyTorch, TensorFlow, and modern distributed systems.
- Proven track record of designing and deploying production-grade Large Language Models (LLMs).
- Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Experience with MLOps tools and methodologies (MLflow, Kubeflow) for model lifecycle management.
- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.