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Information Technology 🏢 Full Time ⭐️ Verified

AI Systems Architect

Nexus Innovations
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Innovations at the forefront of technological evolution as we build the next generation of AI-driven systems. As an AI Systems Architect, you'll design scalable, ethical, and high-performance infrastructure that powers autonomous solutions across healthcare, logistics, and energy sectors. Collaborate with world-class researchers to transform theoretical concepts into production-ready platforms while maintaining rigorous compliance with emerging regulatory frameworks.

Responsibilities

  • Architect end-to-end AI systems integrating quantum-resistant algorithms and federated learning frameworks
  • Lead cross-functional teams in deploying edge-computing solutions for real-time inference
  • Develop governance protocols for autonomous decision-making systems using Explainable AI (XAI) techniques
  • Optimize neural network architectures for energy efficiency and computational sustainability
  • Establish continuous monitoring systems for model drift and bias detection
  • Coordinate with regulatory bodies to ensure compliance with evolving AI safety standards
  • Mentor junior engineers on ethical AI development practices and secure coding methodologies

Qualifications

  • 8+ years in distributed systems architecture with 5+ years specializing in AI/ML infrastructure
  • Expertise in MLOps pipelines using Kubernetes, TensorFlow Extended, and MLflow
  • Published research in federated learning, differential privacy, or algorithmic fairness
  • Professional certification in AI governance (e.g., IEEE Ethically Aligned Design)
  • Experience with low-latency inference frameworks like NVIDIA Triton or ONNX Runtime
  • Proficiency in Python, Go, and Rust for high-performance system development
  • Deep understanding of NIST AI Risk Management Framework and EU AI Act requirements

Required Skills

AI Architecture Quantum Computing Federated Learning MLOps Explainable AI Kubernetes Ethics in AI System Optimization

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