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Senior AI Architect - Future Tech (2026) | San Francisco, CA

Nexus Future Labs
San Francisco
Estimated Salary
USD 165.000 – USD 230.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

We are seeking a visionary Senior AI Architect to lead the development of next-generation intelligent systems. At Nexus Future Labs, we are defining the technological landscape for 2026 and beyond, focusing on autonomous agents, advanced generative models, and quantum-ready algorithms. You will be at the forefront of innovation, architecting scalable solutions that push the boundaries of what is possible in Artificial Intelligence.

Why Join Us?
Work on cutting-edge projects that will shape the future of the industry. We offer a competitive compensation package, equity options, and a remote-first culture that fosters creativity and excellence.

Responsibilities

  • Design and deploy scalable AI infrastructure capable of handling petabyte-scale data.
  • Lead the architecture of Generative AI models and Agentic workflows for enterprise applications.
  • Collaborate with cross-functional teams to integrate AI solutions into complex product ecosystems.
  • Mentor junior engineers and data scientists, fostering a culture of technical excellence.
  • Stay ahead of emerging trends in Deep Learning, NLP, and Computer Vision.
  • Ensure ethical AI practices, data privacy, and robust security standards.

Qualifications

  • Master’s or PhD in Computer Science, Artificial Intelligence, or a related field (5+ years of experience).
  • Deep expertise in Python, PyTorch, and TensorFlow.
  • Proven track record of deploying large-scale Machine Learning models to production.
  • Strong understanding of distributed systems, cloud computing (AWS/GCP), and microservices.
  • Familiarity with MLOps, model versioning, and CI/CD pipelines.
  • Excellent problem-solving skills and the ability to communicate complex technical concepts to stakeholders.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning Cloud Computing MLOps Distributed Systems

Ready to Take This Challenge?

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