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Senior Generative AI Architect (2026 Roadmap)

Nebula AI Systems
Austin
Estimated Salary
USD 160.000 – USD 220.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

We are looking for a visionary Senior Generative AI Architect to join our elite team in Austin. At Nebula AI Systems, we are building the foundational technologies that will define the year 2026 and beyond. You will be at the forefront of developing next-generation Large Language Models (LLMs) and multimodal AI systems that solve complex real-world problems.

If you are passionate about pushing the boundaries of machine learning, optimizing neural architectures, and leading a team of top-tier engineers, we want to hear from you.

Responsibilities

  • Lead the architectural design and implementation of proprietary generative AI models for the 2026 product suite.
  • Optimize model inference performance and scalability using distributed systems and edge computing techniques.
  • Mentor and guide junior engineers and data scientists on best practices in deep learning and NLP.
  • Collaborate with product leadership to define the technical roadmap and deliver innovative AI solutions.
  • Research and evaluate emerging technologies to keep our stack ahead of industry standards.
  • Ensure ethical AI practices and compliance with data privacy regulations in all model deployments.

Qualifications

  • Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related technical field.
  • Minimum of 5+ years of professional experience in AI/ML engineering, with a focus on Generative AI.
  • Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
  • Deep understanding of Transformer architectures, NLP pipelines, and fine-tuning methodologies.
  • Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (MLflow, Kubeflow).
  • Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.

Required Skills

Generative AI Large Language Models PyTorch TensorFlow NLP Machine Learning Engineering MLOps Python Cloud Architecture

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