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Information Technology 🏢 Full Time ⭐️ Verified

Lead AI Infrastructure Architect

Nexus Horizon Solutions
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
25 Mei 2026
Deadline
25 Mei 2027

Job Description

We are seeking a visionary Lead AI Infrastructure Architect to define the technological landscape for 2026 and beyond. In this pivotal role, you will bridge the gap between cutting-edge theoretical AI research and scalable, enterprise-grade deployment.

Join us to architect the neural networks that power the next generation of autonomous systems and intelligent decision-making platforms. You will be responsible for ensuring our infrastructure is not just ready for today, but resilient enough for the rapid evolution of AI by 2026.

Key Highlights:

  • Future-Forward Focus: Work on projects that define the roadmap for AI-native applications.
  • Global Impact: Scale systems that serve millions of users worldwide with zero latency.
  • Elite Team: Collaborate with world-class engineers and data scientists.

Responsibilities

  • Design and deploy high-throughput distributed AI inference systems capable of handling next-generation model loads.
  • Architect quantum-ready hardware integration strategies to future-proof our infrastructure.
  • Lead a team of ML engineers in optimizing model latency, throughput, and cost-efficiency using containerization and orchestration.
  • Establish robust governance frameworks for ethical AI deployment and data privacy compliance.
  • Collaborate with product and engineering leadership to define the technical vision for AI integration.
  • Mentor senior engineering talent and conduct architectural code reviews.

Qualifications

  • 10+ years of experience in software engineering, system architecture, and machine learning infrastructure.
  • Deep expertise in Python, PyTorch, TensorFlow, and Kubernetes.
  • Proven track record of scaling machine learning workloads to millions of concurrent users.
  • Strong understanding of cloud-native technologies (AWS, GCP, Azure) and serverless architectures.
  • Experience with MLOps tools (MLflow, Kubeflow) and large-scale data processing (Spark, Kafka).
  • Excellent communication skills and the ability to translate complex technical concepts for diverse stakeholders.

Required Skills

Python Machine Learning Kubernetes AWS System Architecture PyTorch Cloud Computing MLOps Distributed Systems

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