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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Infrastructure Engineer (2026 Vision)

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
28 Juni 2026
Deadline
28 Jun 2027

Job Description

Are you ready to architect the digital landscape of tomorrow? Nexus Future Labs is seeking a visionary Senior AI Infrastructure Engineer to lead the development of scalable, high-performance systems for our aggressive 2026 roadmap. In this pivotal role, you will bridge the gap between cutting-edge theoretical AI research and production-grade infrastructure, ensuring our platforms are resilient, efficient, and ready for the future of computing.

Join a team of world-class engineers and researchers dedicated to pushing the boundaries of Generative AI, Neural Networks, and Autonomous Systems. You will have the autonomy to define architectural standards and mentor a growing team of infrastructure specialists.

Responsibilities

  • Design and deploy highly scalable distributed machine learning pipelines and inference engines capable of handling exabyte-scale data.
  • Optimize GPU cluster configurations and resource allocation strategies to maximize model training throughput and minimize latency.
  • Implement robust CI/CD pipelines for AI model deployment, ensuring automated testing, validation, and rollback protocols.
  • Collaborate with data scientists to translate research prototypes into stable, production-ready microservices.
  • Ensure the security, compliance, and data sovereignty of all AI workloads across cloud and edge environments.
  • Conduct architecture reviews and drive technical decisions that align with long-term strategic goals.

Qualifications

  • 7+ years of experience in software engineering, DevOps, or MLOps with a strong focus on infrastructure.
  • Deep expertise in Python, Go, or Rust with proven experience in building high-performance systems.
  • Hands-on experience with containerization (Docker, Kubernetes) and orchestration (EKS, GKE, AKS).
  • Proficiency in major ML frameworks such as PyTorch, TensorFlow, or JAX.
  • Strong background in cloud infrastructure (AWS, GCP, or Azure) and serverless architectures.
  • Familiarity with GPU virtualization technologies (e.g., vGPU, CUDA) and high-performance computing (HPC) environments.
  • Excellent problem-solving skills and the ability to thrive in a fast-paced, agile environment.

Required Skills

Python Go Rust Docker Kubernetes PyTorch TensorFlow AWS GCP Machine Learning MLOps System Design GPU Clusters

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