Job Description
Join the Architects of 2026
Nexus Future Labs is at the forefront of defining the AI landscape for the coming decade. We are seeking a visionary Senior AI Architect to lead the design and deployment of next-generation Generative AI systems. In this role, you will not just build models; you will architect the infrastructure that powers autonomous, multi-modal AI agents expected to dominate the industry in 2026 and beyond.
Why This Role?
As we push the boundaries of Artificial General Intelligence (AGI) readiness, we need a leader who understands the intersection of deep learning theory, scalable distributed systems, and ethical AI implementation.
Core Responsibilities
- Architect and deploy scalable LLM (Large Language Model) infrastructure tailored for high-frequency inference environments.
- Design and optimize post-Transformer neural architectures focusing on long-horizon reasoning and multi-modal synthesis.
- Establish best practices for Agentic AI workflows, ensuring agents can autonomously plan and execute complex tasks.
- Collaborate with data scientists and engineering teams to refine data pipelines and fine-tuning strategies for niche domain applications.
- Lead the implementation of robust safety and alignment protocols to mitigate hallucinations and ensure regulatory compliance in 2026 standards.
- Mentor junior architects and engineers, fostering a culture of innovation and technical excellence.
Qualifications
- Master’s or Ph.D. in Computer Science, Mathematics, or a related technical field with 8+ years of experience in machine learning engineering.
- Deep expertise in training, fine-tuning, and serving Large Language Models (LLMs) and diffusion models.
- Proficiency in Python, PyTorch, TensorFlow, and modern inference engines (e.g., vLLM, SGLang, TensorRT-LLM).
- Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Proven track record of leading cross-functional teams in high-stakes software delivery.
- Experience with MLOps tools and CI/CD pipelines for machine learning.
What We Offer
Competitive compensation package, equity options, and a flexible remote-first culture.
Responsibilities
- Architect and deploy scalable LLM (Large Language Model) infrastructure tailored for high-frequency inference environments.
- Design and optimize post-Transformer neural architectures focusing on long-horizon reasoning and multi-modal synthesis.
- Establish best practices for Agentic AI workflows, ensuring agents can autonomously plan and execute complex tasks.
- Collaborate with data scientists and engineering teams to refine data pipelines and fine-tuning strategies for niche domain applications.
- Lead the implementation of robust safety and alignment protocols to mitigate hallucinations and ensure regulatory compliance in 2026 standards.
- Mentor junior architects and engineers, fostering a culture of innovation and technical excellence.
Qualifications
- Master’s or Ph.D. in Computer Science, Mathematics, or a related technical field with 8+ years of experience in machine learning engineering.
- Deep expertise in training, fine-tuning, and serving Large Language Models (LLMs) and diffusion models.
- Proficiency in Python, PyTorch, TensorFlow, and modern inference engines (e.g., vLLM, SGLang, TensorRT-LLM).
- Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Proven track record of leading cross-functional teams in high-stakes software delivery.
- Experience with MLOps tools and CI/CD pipelines for machine learning.