Job Description
Are you ready to architect the technology of 2026? Nexus Future Tech is pioneering the next generation of autonomous systems and Artificial General Intelligence (AGI). We are looking for a visionary Senior AI Architect to lead our research and engineering division. In this pivotal role, you will design scalable neural architectures, optimize real-time inference systems, and define the ethical frameworks that will govern the future of human-machine interaction. Join us in building the intelligent infrastructure of tomorrow, today.
We offer a competitive compensation package, equity packages, and the opportunity to work on cutting-edge projects that will shape the industry for years to come. If you are passionate about pushing the boundaries of what is possible in AI, we want to hear from you.
We offer a competitive compensation package, equity packages, and the opportunity to work on cutting-edge projects that will shape the industry for years to come. If you are passionate about pushing the boundaries of what is possible in AI, we want to hear from you.
Responsibilities
- Architect and deploy state-of-the-art Large Language Models (LLMs) and multimodal agents.
- Design robust pipelines for data ingestion, training, and MLOps operations.
- Collaborate with product teams to integrate advanced AI capabilities into consumer applications.
- Conduct rigorous research on reinforcement learning, causal inference, and memory mechanisms.
- Establish safety protocols and ethical guidelines for autonomous decision-making systems.
- Mentor a team of brilliant engineers and data scientists in advanced AI techniques.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 5+ years of professional experience in AI/ML engineering or research.
- Deep expertise in Python, PyTorch, and TensorFlow.
- Proven track record of optimizing model performance and reducing inference latency.
- Experience with vector databases and semantic search technologies.
- Strong understanding of transformer architectures and attention mechanisms.