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Senior AI Architect (Agentic Systems)

Zenith Future Labs
San Francisco
Estimated Salary
USD 160.000 – USD 240.000
Live Update
24 Mei 2026
Deadline
24 Mei 2027

Job Description

We are at the precipice of the AI revolution. As we look toward 2026, the integration of Agentic AI and Autonomous Decision Systems is set to redefine industries. Zenith Future Labs is seeking a visionary Senior AI Architect to lead our next-generation research division.

In this role, you won't just build models; you will architect the brain of tomorrow's autonomous agents. You will work with cutting-edge Large Language Models (LLMs), Reinforcement Learning (RL), and Edge Computing paradigms to solve complex, real-world problems.

Why join us? We offer a competitive package, equity packages, and the opportunity to shape the future of technology in a remote-first, high-performance environment.

Responsibilities

  • Design and deploy scalable, production-grade AI agents capable of autonomous decision-making.
  • Lead the research and implementation of next-generation Generative AI models.
  • Optimize model inference latency for edge devices and distributed systems.
  • Establish best practices for AI ethics, safety, and bias mitigation.
  • Collaborate with cross-functional engineering teams to integrate AI into core product pipelines.
  • Conduct deep research into novel algorithms and mathematical models.

Qualifications

  • Ph.D. or Master's degree in Computer Science, Mathematics, or a related field.
  • 5+ years of experience in Deep Learning, Natural Language Processing (NLP), or Reinforcement Learning.
  • Expert proficiency in Python, PyTorch, or TensorFlow.
  • Experience with LLM fine-tuning and RAG (Retrieval-Augmented Generation) architectures.
  • Strong understanding of distributed computing and cloud infrastructure (AWS/GCP/Azure).
  • Track record of publishing in top-tier AI conferences (NeurIPS, ICML, etc.) or leading open-source contributions.

Required Skills

Python PyTorch TensorFlow Deep Learning NLP LLM Reinforcement Learning MLOps Cloud Computing

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