Job Description
Are you ready to define the future of intelligent systems? Nexus Future Tech is seeking a visionary Senior AI Architect to spearhead our 2026 strategic roadmap. As we stand on the precipice of a new era in artificial general intelligence, we need a leader who can bridge the gap between theoretical research and scalable production systems. You will be instrumental in architecting the next generation of autonomous agents and predictive models that will power enterprise solutions for the decade ahead.
Why Join Us?
- Work on bleeding-edge technology that shapes the 2026 landscape.
- Competitive compensation package and equity options.
- Flexible remote-first policy with a hub in the heart of SF.
- Access to state-of-the-art compute infrastructure.
The Role:
We are looking for a technical expert who understands not just the code, but the implication of the code. In this role, you will design robust, secure, and ethical AI frameworks. You will collaborate with cross-functional teams to integrate AI capabilities into consumer and B2B platforms, ensuring scalability and performance.
Responsibilities
- Architect and design scalable AI models and neural networks for the 2026 product suite.
- Lead research initiatives in Generative AI, Natural Language Processing, and Computer Vision.
- Optimize model inference latency and reduce resource consumption for edge deployment.
- Establish best practices for MLOps, data governance, and model monitoring.
- Mentor junior engineers and data scientists to foster a culture of innovation.
- Stay ahead of industry trends to recommend emerging technologies for integration.
- Collaborate with product managers to translate complex AI capabilities into user-centric features.
Qualifications
- Master’s degree or PhD in Computer Science, AI, or a related quantitative field.
- Minimum of 5+ years of professional experience in AI/ML engineering and architecture.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying large-scale machine learning models into production environments.
- Deep understanding of deep learning architectures, transformers, and reinforcement learning.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Excellent problem-solving skills and ability to communicate complex technical concepts to non-technical stakeholders.