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Information Technology 🏢 Full Time ⭐️ Verified

Senior AI Infrastructure Architect

Nexus Horizon
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

Join Nexus Horizon, a pioneering force in the 2026 technology landscape. We are seeking a visionary Senior AI Infrastructure Architect to design the backbone of our next-generation neural networks and scalable AI ecosystems.

In this pivotal role, you will bridge the gap between theoretical AI research and production-grade infrastructure. You will be responsible for architecting systems that are not only powerful but also resilient, ethical, and future-proof.

As we gear up for the next era of digital transformation, your expertise in high-performance computing and large language model (LLM) deployment will be critical to our success.

Responsibilities

  • Architect and deploy scalable AI infrastructure for large-scale language models and generative AI applications.
  • Optimize cloud-native environments (AWS/Azure/GCP) to maximize inference speed and reduce latency.
  • Collaborate with data scientists to translate research prototypes into robust, production-ready systems.
  • Implement robust security protocols and ethical AI governance frameworks.
  • Lead technical strategy for the integration of quantum-ready computing protocols.
  • Conduct performance tuning and load balancing for distributed neural networks.

Qualifications

  • Bachelor’s degree in Computer Science, Mathematics, or a related field; Master’s degree preferred.
  • 7+ years of experience in software engineering, machine learning operations (MLOps), or cloud architecture.
  • Deep expertise in Python, TensorFlow, PyTorch, and Keras.
  • Proven track record of deploying LLMs at scale with high availability.
  • Strong understanding of distributed systems, microservices, and containerization (Docker/Kubernetes).
  • Experience with vector databases (Pinecone, Milvus) and RAG architectures.

Required Skills

Python Machine Learning Cloud Computing AWS Kubernetes MLOps Deep Learning Neural Networks System Design

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