Job Description
Are you ready to shape the future of technology? Nexus Future Systems is seeking a visionary Senior AI Architect to lead our groundbreaking Project 2026. This is not just another engineering role; it is a mission to define the next generation of Generative Intelligence and Quantum-Enhanced Machine Learning.
In this pivotal position, you will architect scalable neural architectures and pioneer algorithms that push the boundaries of what is currently possible. You will work at the intersection of theoretical research and practical application, ensuring our solutions are robust, efficient, and revolutionary.
Why Join Us?
- Work on a mission-critical initiative that defines the roadmap for 2026 and beyond.
- Competitive compensation package with equity options.
- State-of-the-art research facilities and cutting-edge hardware.
- Collaborative environment with industry leaders in AI and Quantum Computing.
Responsibilities
- Design and implement scalable, high-performance AI architectures for the Project 2026 initiative, focusing on Generative AI and Large Language Models (LLMs).
- Lead the research and development of novel algorithms to improve model accuracy and reduce computational overhead.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-ready solutions.
- Mentor junior engineers and conduct technical architecture reviews to ensure code quality and adherence to best practices.
- Stay abreast of the latest advancements in AI, deep learning, and quantum computing to integrate cutting-edge techniques into our pipeline.
- Optimize existing models for latency, throughput, and resource efficiency in cloud and edge environments.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related field, with a focus on Artificial Intelligence or Machine Learning.
- 8+ years of professional experience in software engineering, with at least 5 years specializing in AI/ML architecture.
- Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong proficiency in programming languages including Python, C++, and CUDA.
- Demonstrated experience deploying large-scale machine learning models in production environments.
- Experience with distributed computing systems (e.g., Kubernetes, Docker) and cloud platforms (AWS, GCP, or Azure).
- Excellent problem-solving skills and the ability to thrive in a fast-paced, high-impact environment.