Job Description
We are not just predicting the future; we are architecting it. At Nexus Core Systems, we are at the forefront of the 2026 technological renaissance, building the infrastructure for General Artificial Intelligence and Quantum-Neural integration. As a Senior AI Architect, you will lead the development of our proprietary Large Language Models and multi-modal reasoning engines. This is a high-impact role for a visionary engineer who thrives in ambiguity and wants to define the standard for next-gen computing.
Why join us?
We offer competitive compensation, equity packages, and the opportunity to work on projects that will define the industry standards for the upcoming decade.
Core Responsibilities:
You will be responsible for designing scalable AI architectures, optimizing deep learning pipelines, and ensuring our systems remain resilient against adversarial attacks in a decentralized network.
Why join us?
We offer competitive compensation, equity packages, and the opportunity to work on projects that will define the industry standards for the upcoming decade.
Core Responsibilities:
You will be responsible for designing scalable AI architectures, optimizing deep learning pipelines, and ensuring our systems remain resilient against adversarial attacks in a decentralized network.
Responsibilities
- Architect and deploy scalable deep learning models capable of real-time inference on distributed hardware.
- Lead the technical strategy for the '2026 Horizon' initiative, focusing on AGI alignment and safety protocols.
- Collaborate with quantum computing researchers to integrate probabilistic algorithms into neural networks.
- Mentor a team of junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Design robust data pipelines to process petabytes of unstructured data for model training.
- Conduct rigorous performance benchmarking to ensure our AI solutions outperform current market leaders.
Qualifications
- Masters or PhD in Computer Science, Artificial Intelligence, Physics, or a related technical field.
- 5+ years of professional experience in machine learning engineering, deep learning, or applied AI research.
- Extensive experience with Python, PyTorch, TensorFlow, and modern MLOps tools (Kubernetes, Docker, MLflow).
- Strong understanding of Transformer architectures, Reinforcement Learning, and Generative Adversarial Networks (GANs).
- Proven track record of shipping production-grade AI systems that handle high concurrency and low latency.