Job Description
The year 2026 is on the horizon, representing a pivotal moment in the evolution of Artificial General Intelligence (AGI) and autonomous systems. At Nexus Future Labs, we are not just preparing for the future; we are architecting it. We are seeking a visionary Lead AI Architect to spearhead our strategic initiatives, defining the technical roadmap that will define the next generation of intelligent machines.
If you are passionate about pushing the boundaries of what is possible in AI, safety, and scalability, we want to hear from you. This is a unique opportunity to build the infrastructure for the 2026 era of technology.
Why Join Us?
• Work on cutting-edge Agentic AI systems.
• Shape the technical standards for the next generation of technology.
• Competitive compensation and equity package.
• Flexible remote-first culture.
Responsibilities
- Architect and define the core AI infrastructure roadmap for the 2026 release cycle, focusing on scalability and safety.
- Lead a cross-functional team of ML engineers and researchers to develop next-gen Large Language Models (LLMs) and agents.
- Ensure architectural integrity, performance optimization, and security compliance across all AI products.
- Collaborate with product leadership to translate business goals into technical specifications.
- Drive technical innovation, including patent generation and thought leadership in the AI community.
- Establish best practices for AI model deployment and MLOps pipelines.
- Mentor junior engineers and foster a culture of continuous learning and technical excellence.
Qualifications
- PhD or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- 10+ years of experience in software engineering and deep learning architecture.
- Proven track record of leading high-impact engineering teams and managing large-scale projects.
- Expert proficiency in Python, PyTorch, TensorFlow, and distributed systems.
- Strong background in Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Deep understanding of AI safety, alignment research, and ethical AI principles.