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Information Technology 🏒 Full Time ⭐️ Verified

Senior AI Architect (2026 Horizon)

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
25 Mei 2026
Deadline
25 Mei 2027

Job Description

Are you ready to define the technological landscape of 2026? Nexus Future Labs is seeking a visionary Senior AI Architect to lead our R&D division. We are building the infrastructure for the next generation of intelligent systems, and we need a pioneer who can bridge the gap between theoretical AI and scalable production.

In this pivotal role, you will not just write code; you will architect the future. You will be responsible for the strategic direction of our flagship '2026' AI platform, ensuring it remains at the cutting edge of machine learning, neural networks, and generative technologies.

Responsibilities

  • Architect and lead the development of the '2026' core engine, ensuring high performance, scalability, and security.
  • Define the technical roadmap and engineering standards for the AI division.
  • Collaborate with product managers to translate futuristic concepts into actionable technical specifications.
  • Implement and optimize MLOps pipelines to automate model training and deployment.
  • Conduct deep-dive research into emerging AI trends to integrate cutting-edge technologies (e.g., quantum computing interfaces, advanced NLP).
  • Mentor a team of elite data scientists and engineers, fostering a culture of innovation and excellence.

Qualifications

  • Master’s degree in Computer Science, Artificial Intelligence, or a related STEM field (PhD preferred).
  • 10+ years of experience in software architecture, with at least 5 years focused on AI/ML.
  • Expert proficiency in Python, TensorFlow, PyTorch, and modern cloud infrastructure (AWS/GCP/Azure).
  • Proven track record of leading large-scale projects from concept to deployment.
  • Strong understanding of ethical AI, data privacy, and responsible algorithmic design.

Required Skills

Python TensorFlow PyTorch AWS Machine Learning Deep Learning System Architecture MLOps Leadership

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