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Lead AI Architect: 2026 Roadmap (San Francisco, CA)

Nexus Future Systems
San Francisco
Estimated Salary
USD 190.000 – USD 280.000
New
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

Join the Vanguard of 2026 Technology

Nexus Future Systems is pioneering the next generation of intelligent infrastructure. We are seeking a visionary Lead AI Architect to define the technical roadmap for our 2026 product suite. This is a rare opportunity to shape the future of Generative AI and Large Language Model (LLM) integration in enterprise workflows.

Why Join Us?

As we approach the 2026 tech horizon, we are building a team that redefines scalability and ethical AI. You will work with cutting-edge hardware and software stacks, mentoring a world-class team of engineers dedicated to pushing the boundaries of what is possible.

Key Responsibilities:

  • Define the 2026 Technical Roadmap: Lead the architectural vision for next-gen AI systems, ensuring scalability, security, and performance for the 2026 product cycle.
  • Model Optimization & Deployment: Design and implement efficient pipelines for training and deploying Large Language Models (LLMs) and multimodal AI agents.
  • Infrastructure Strategy: Oversee the architecture of high-performance GPU clusters and cloud-native environments to support real-time inference at scale.
  • Mentorship & Culture: Foster a culture of innovation, guiding junior engineers and data scientists in best practices for AI development.
  • Cross-Functional Leadership: Collaborate with product managers and security experts to integrate AI capabilities seamlessly into user experiences.
  • R&D Leadership: Conduct research into emerging AI paradigms to keep Nexus Future Systems ahead of industry trends.

Qualifications:

  • Advanced Degree: MS or PhD in Computer Science, Machine Learning, or a related field (or equivalent practical experience).
  • Technical Mastery: Extensive experience with Python, PyTorch, TensorFlow, or JAX, and deep knowledge of deep learning architectures.
  • LLM Expertise: Proven track record in developing, fine-tuning, or deploying Large Language Models (e.g., GPT, Llama, Claude).
  • System Design: Strong background in distributed systems, microservices, and high-availability architecture.
  • Cloud Native: Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
  • Leadership: Demonstrated ability to lead engineering teams and drive technical strategy from conception to delivery.

Responsibilities

  • Lead the architectural vision for next-gen AI systems, ensuring scalability, security, and performance for the 2026 product cycle.
  • Design and implement efficient pipelines for training and deploying Large Language Models (LLMs) and multimodal AI agents.
  • Oversee the architecture of high-performance GPU clusters and cloud-native environments to support real-time inference at scale.
  • Foster a culture of innovation, guiding junior engineers and data scientists in best practices for AI development.
  • Collaborate with product managers and security experts to integrate AI capabilities seamlessly into user experiences.
  • Conduct research into emerging AI paradigms to keep Nexus Future Systems ahead of industry trends.

Qualifications

  • MS or PhD in Computer Science, Machine Learning, or a related field (or equivalent practical experience).
  • Extensive experience with Python, PyTorch, TensorFlow, or JAX, and deep knowledge of deep learning architectures.
  • Proven track record in developing, fine-tuning, or deploying Large Language Models (e.g., GPT, Llama, Claude).
  • Strong background in distributed systems, microservices, and high-availability architecture.
  • Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
  • Demonstrated ability to lead engineering teams and drive technical strategy from conception to delivery.

Required Skills

Python Machine Learning Generative AI LLMs Deep Learning TensorFlow PyTorch System Design Cloud Architecture Kubernetes AWS GCP

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