Job Description
We are seeking a visionary Principal AI Architect to define the technical trajectory for Nexus Horizon AI through 2026 and beyond. As the landscape of artificial intelligence evolves from simple chatbots to autonomous, agentic systems, we need a leader who can architect the foundational infrastructure for the next industrial revolution.
In this role, you will be the bridge between theoretical AI research and scalable production systems. You will lead the design of next-generation Large Language Models (LLM 2.0), autonomous decision-making agents, and neural interfaces. If you are passionate about shaping the future of technology and building systems that think, this is your opportunity to lead the charge.
Why Join Us?
- Work on cutting-edge Agentic AI and Quantum-inspired computing projects.
- Competitive compensation package and equity options.
- Flexible remote-first culture with a hub in San Francisco.
Responsibilities
- Architect and design the 2026 AI Infrastructure Roadmap, focusing on autonomous agent workflows and multimodal LLMs.
- Lead the technical strategy for MLOps pipelines, ensuring scalability, security, and high-performance inference.
- Collaborate with research teams to integrate novel neural architectures into production environments.
- Define and implement ethical AI safety protocols and bias mitigation strategies for generative models.
- Oversee the migration of legacy systems to edge-computing paradigms for real-time AI processing.
- Mentor senior engineering teams and establish architectural standards for distributed AI systems.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- 10+ years of experience in Machine Learning Engineering, with at least 5 years in a lead or architect role.
- Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
- Proven track record of deploying production-grade LLMs and Generative AI solutions.
- Strong understanding of system architecture, cloud infrastructure (AWS/GCP/Azure), and Kubernetes.
- Familiarity with the ethical implications and safety measures required for advanced AI deployment.