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Information Technology 🏢 Full Time ⭐️ Verified

AI Infrastructure Architect (2026 Vision)

Nexus Horizon Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

We are seeking a visionary AI Infrastructure Architect to lead the charge in building the resilient, scalable backbone for next-generation artificial intelligence systems. In the rapidly evolving landscape of 2026, infrastructure is the new frontier.

At Nexus Horizon Labs, we are not merely preparing for the future; we are architecting it. You will be at the helm of deploying quantum-ready networks, real-time neural processing hubs, and ethically governed AI frameworks. If you thrive in high-stakes environments and possess a passion for pushing the boundaries of technology, this role is for you.

Responsibilities

  • Architect Scalable Pipelines: Design and implement robust machine learning infrastructure capable of handling petabyte-scale data streams and real-time inference.
  • Quantum-Ready Systems: Collaborate with quantum computing researchers to optimize algorithms for hybrid cloud environments and post-silicon computing architectures.
  • Security & Compliance: Enforce rigorous security protocols to protect sensitive neural models and ensure compliance with emerging global AI governance laws.
  • Team Leadership: Mentor a high-performance team of DevOps engineers and Data Scientists, fostering a culture of innovation and technical excellence.
  • Performance Optimization: Continuously monitor system health, tuning latency and throughput to ensure seamless user experiences for critical AI applications.

Qualifications

  • Experience: Minimum of 7 years of experience in Software Engineering, with a specific focus on AI/ML infrastructure and DevOps.
  • Core Stack: Deep proficiency in Python, TensorFlow, PyTorch, and distributed systems (Kubernetes, Docker, Apache Kafka).
  • Cloud Expertise: Strong background in cloud architecture (AWS, GCP, or Azure) and serverless computing models.
  • Edge Computing: Experience deploying AI models at the edge of the network for low-latency applications.
  • Communication: Exceptional ability to translate complex technical concepts into actionable business strategies.

Required Skills

Python TensorFlow Kubernetes AWS Edge Computing AI Ethics Machine Learning DevOps Cloud Architecture Distributed Systems

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