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Information Technology 🏒 Full Time ⭐️ Verified

Lead AI Architect | San Francisco, CA

Nexus Horizon Systems
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
5 Juni 2026
Deadline
5 Jun 2027

Job Description

Are you ready to define the future of intelligence? Nexus Horizon Systems is seeking a visionary Lead AI Architect to spearhead our research into Artificial General Intelligence (AGI). In this pivotal role, you will architect scalable neural networks, optimize large language models, and lead a team of elite engineers pushing the boundaries of what's possible in 2026. You will be responsible for the technical vision, architecture design, and execution of our core AI products, ensuring they remain at the cutting edge of innovation.

Responsibilities

  • Design and implement scalable AI/ML infrastructure capable of handling next-gen workloads.
  • Lead research initiatives in Natural Language Processing (NLP), computer vision, and multimodal learning.
  • Optimize model inference latency and accuracy for both cloud and edge deployment scenarios.
  • Mentor and guide a high-performing engineering team in best practices and advanced algorithms.
  • Collaborate with product stakeholders to translate complex AI concepts into market-ready features.
  • Establish and enforce ethical AI governance, safety protocols, and bias mitigation strategies.

Qualifications

  • Master’s or PhD in Computer Science, Machine Learning, or a related technical field.
  • 7+ years of experience in designing and deploying production-level AI systems.
  • Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
  • Proven track record of deploying large-scale LLMs into production environments.
  • Strong understanding of MLOps, data pipelines, and cloud architecture (AWS/GCP/Azure).
  • Excellent communication skills and the ability to thrive in a fast-paced, innovative environment.

Required Skills

Python TensorFlow PyTorch NLP MLOps System Design AWS GCP Distributed Systems AGI Machine Learning

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