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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Infrastructure Engineer (2026 Vision)

Nexus Horizon
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

Nexus Horizon is at the forefront of the artificial intelligence revolution, pioneering the systems that will define the future of work. We are seeking a visionary Senior AI Infrastructure Engineer to architect the scalable backbone for our next-generation large language models and autonomous agents.

In this pivotal role, you will bridge the gap between cutting-edge research and production-grade deployment, ensuring our AI solutions are robust, efficient, and ready for the demands of 2026 and beyond. You will work in a dynamic environment where innovation is not just encouraged—it is the standard.

Key Highlights:

  • Impact: Directly influence the architecture of systems that power millions of users.
  • Culture: Join a diverse team of engineers, researchers, and ethicists committed to responsible AI.
  • Compensation: Competitive salary, equity package, and comprehensive benefits.

Responsibilities

  • Design and implement high-throughput, low-latency inference pipelines for large-scale LLMs.
  • Optimize model serving architectures to handle millions of concurrent requests while minimizing latency.
  • Collaborate closely with ML researchers to translate experimental models into production-ready services.
  • Implement robust monitoring, logging, and observability stacks (e.g., Prometheus, Grafana, ELK) for complex AI workloads.
  • Ensure strict data privacy, security compliance, and ethical AI deployment standards.
  • Drive technical strategy for cloud infrastructure scaling and multi-region cost optimization.
  • Mentor junior engineers and establish best practices for MLOps and DevOps within the AI team.

Qualifications

  • 5+ years of experience in software engineering, with a specific focus on machine learning infrastructure.
  • Deep proficiency in Python, TensorFlow, PyTorch, and CUDA.
  • Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Kubernetes, Docker).
  • Experience with MLOps tools such as MLflow, Kubeflow, or Airflow.
  • Strong understanding of distributed systems, message queues, and microservices architecture.
  • Excellent problem-solving skills and the ability to thrive in a fast-paced, agile startup environment.

Required Skills

Python Machine Learning Kubernetes AWS PyTorch MLOps Distributed Systems Docker TensorFlow

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