Job Description
We are looking for a visionary Principal AI Architect to lead the charge in building the autonomous intelligence infrastructure for 2026. At Nexus Future Labs, we aren't just keeping up with the AI revolution; we are defining its future. As the industry moves towards advanced Agentic AI and Spatial Computing, you will be at the helm of designing scalable, secure, and high-performance systems.
In this role, you will bridge the gap between cutting-edge research and production-grade engineering, ensuring our platforms are ready for the next generation of Large Autonomous Agents. If you are passionate about the convergence of LLMs, vector databases, and real-time decision-making, this is your opportunity to shape the technology stack of tomorrow.
Responsibilities
- Design and architect end-to-end Agentic AI workflows and multi-agent systems capable of complex, autonomous decision-making.
- Lead the technical strategy for Generative AI deployment, focusing on inference optimization and cost-efficiency at scale.
- Oversee the integration of Spatial Computing interfaces into our AI ecosystem to create immersive user experiences.
- Establish best practices for AI ethics, safety, and explainability within the development lifecycle.
- Mentor senior engineering teams and drive architectural decisions that align with our 5-year product roadmap.
- Collaborate with cross-functional partners (Product, Research, Legal) to define technical requirements for upcoming releases.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field (PhD preferred).
- 10+ years of experience in software engineering with at least 5 years focused on Machine Learning and AI infrastructure.
- Deep expertise in PyTorch, TensorFlow, or similar frameworks, with proven experience deploying LLMs in production.
- Strong understanding of vector databases (Pinecone, Milvus) and RAG (Retrieval-Augmented Generation) architectures.
- Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).
- Ability to translate abstract research concepts into robust, scalable engineering solutions.