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Senior Generative AI Architect (2026 Roadmap)

Nexus Horizon AI
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
7 Juli 2026
Deadline
7 Jul 2027

Job Description

We are seeking a visionary Senior Generative AI Architect to join our elite engineering team. As we define the 2026 Roadmap, you will be at the forefront of building next-generation Large Language Models (LLMs) and multimodal systems. This is not just a coding role; it is a strategic position to shape the future of AI interaction.

Why Join Us?

We are backed by top-tier venture capital and are scaling rapidly to redefine the enterprise AI landscape. You will work with state-of-the-art hardware and collaborate with world-class researchers to solve complex problems in reasoning, safety, and scalability.

Responsibilities

  • Architect and deploy scalable Generative AI models, including LLMs and diffusion models, optimized for production environments.
  • Lead the end-to-end development lifecycle of AI features, from data ingestion and preprocessing to fine-tuning and evaluation.
  • Implement advanced Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
  • Collaborate with cross-functional teams of data scientists, ML engineers, and product managers to translate research into production-ready features.
  • Mentor junior engineers, conducting code reviews and establishing best practices for AI model deployment and monitoring.
  • Ensure compliance with ethical AI guidelines and data privacy regulations (GDPR, CCPA).

Qualifications

  • Master’s degree or PhD in Computer Science, Mathematics, or a related field, with a focus on Deep Learning or Natural Language Processing.
  • Minimum of 5+ years of experience in machine learning engineering, with at least 2 years specifically in Generative AI or LLMs.
  • Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
  • Strong understanding of transformer architectures, attention mechanisms, and model optimization techniques.
  • Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
  • Demonstrated ability to optimize model latency and throughput for real-time applications.

Required Skills

Python PyTorch TensorFlow Large Language Models LLM Deep Learning MLOps Machine Learning NLP AWS GCP Kubernetes Docker Transformer Models RAG Fine-tuning Generative AI

Ready to Take This Challenge?

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