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Future Tech Architect (2026 Roadmap)

Quantum Horizon Systems
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

We are looking for a visionary Future Tech Architect to lead the development of our strategic '2026' initiative. As the landscape of artificial intelligence evolves, we need a leader who can bridge the gap between theoretical breakthroughs and scalable production systems.

In this role, you will define the architectural blueprint for autonomous agents and advanced predictive models. You will work closely with R&D teams to implement cutting-edge technologies that will define the next decade of digital intelligence. If you are passionate about building the future and want to leave a lasting legacy in the tech industry, we want to hear from you.

Responsibilities

  • Architect Autonomous Systems: Design and implement robust, self-improving AI agents capable of complex decision-making and autonomous task execution.
  • Lead the 2026 Roadmap: Define the technical vision and milestones for the company's major product releases in the 2026 timeframe.
  • Optimize Inference: Work on high-performance computing environments to optimize model inference speeds and reduce latency.
  • Research Integration: Stay at the forefront of AI research, evaluating and integrating emerging technologies such as Multi-Agent Systems and Federated Learning.
  • Technical Mentorship: Mentor junior engineers and data scientists, fostering a culture of innovation and excellence within the engineering team.
  • Scalability Strategy: Ensure that our AI infrastructure can scale horizontally to meet growing enterprise demands.

Qualifications

  • Education: PhD or Master's degree in Computer Science, Mathematics, or a related field.
  • Experience: 5+ years of experience in software engineering, with at least 3 years specifically focused on AI/ML systems architecture.
  • Programming: Proficiency in Python, C++, and Rust. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
  • Domain Knowledge: Deep understanding of Large Language Models (LLMs), Transformers, and Reinforcement Learning from Human Feedback (RLHF).
  • System Design: Proven track record of designing scalable, fault-tolerant systems for high-traffic applications.
  • Communication: Exceptional ability to communicate complex technical concepts to stakeholders and non-technical audiences.

Required Skills

Python C++ Rust PyTorch TensorFlow Machine Learning Deep Learning System Design MLOps Large Language Models Autonomous Agents Cloud Computing (AWS/GCP)

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