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Senior AI Architect (Generative AI) - 2026 Vision

NeuralCore Systems
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
6 Juni 2026
Deadline
6 Jun 2027

Job Description

We are on a mission to define the AI landscape of 2026. As a Senior AI Architect at NeuralCore, you will lead the technical strategy for our next-generation generative intelligence platform. We are looking for a visionary engineer to build scalable, efficient, and safe foundation models that power autonomous agents and multimodal applications.


What You Will Do:

Shape the future of artificial intelligence by architecting robust systems that push the boundaries of what is possible. You will work at the intersection of research and production, ensuring our models are not only state-of-the-art but also production-ready.

Responsibilities

  • Lead System Architecture: Design and implement the core infrastructure for Large Language Models (LLMs) and multimodal systems, optimizing for performance and cost.
  • Autonomous Agents: Develop the architectural patterns for autonomous AI agents, enabling them to perform complex reasoning and tool usage.
  • MLOps & Scalability: Build and maintain high-throughput MLOps pipelines to support continuous training, fine-tuning, and deployment of models.
  • Ethical AI: Implement safety guardrails and bias mitigation strategies to ensure responsible AI deployment.
  • Research Collaboration: Collaborate with data scientists to translate cutting-edge research findings into scalable production code.
  • Technical Leadership: Mentor junior engineers and set technical standards for the AI engineering team.

Qualifications

  • Education: Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on Machine Learning or AI.
  • Experience: 5+ years of experience in software engineering, with at least 3 years specifically in AI/ML architecture.
  • Core Skills: Deep expertise in Python, PyTorch, and TensorFlow. Strong understanding of Transformer architectures and attention mechanisms.
  • Infrastructure: Experience designing systems using cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
  • Tools: Proficiency in MLOps tools (MLflow, Kubeflow) and vector databases (Pinecone, Milvus, Weaviate).
  • Problem Solving: Demonstrated ability to tackle complex technical challenges and drive innovative solutions in a fast-paced environment.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning LLMs Generative AI MLOps Cloud Computing Kubernetes Docker AWS GCP NLP

Ready to Take This Challenge?

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