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Information Technology 🏢 Full Time ⭐️ Verified

Senior Generative AI Engineer (2026 Vision)

Nexus Future Systems
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Are you ready to engineer the breakthrough technologies of 2026? Nexus Future Systems is seeking a visionary Senior Generative AI Engineer to build the next generation of cognitive models. In this role, you won't just write code; you will define the architecture of future intelligence. We are looking for a pioneer who thrives on complex challenges and possesses the expertise to navigate the cutting edge of Large Language Models (LLMs) and generative architectures.

We are on a mission to redefine human-computer interaction, and we need a technical leader to drive our generative initiatives forward. If you are passionate about the ethical and technical implications of AI and want to work in a high-growth environment, apply today.

Responsibilities

  • Architect and deploy scalable Generative AI models (LLMs, diffusion, etc.) for production environments with a focus on 2026 readiness.
  • Optimize model inference speed and reduce latency using quantization, pruning, and efficient transformer architectures.
  • Develop and implement advanced Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and context retention.
  • Collaborate with cross-functional teams (Product, Design, Data Science) to translate business requirements into advanced technical solutions.
  • Perform rigorous testing and validation of AI models to ensure safety, fairness, and robustness in real-world scenarios.
  • Stay ahead of the curve by researching emerging AI architectures and methodologies.

Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field.
  • 5+ years of experience in software engineering with a strong focus on machine learning or AI.
  • Proficiency in Python, PyTorch, or TensorFlow with a deep understanding of deep learning frameworks.
  • Deep understanding of Transformer architectures, attention mechanisms, and generative adversarial networks.
  • Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) and cloud infrastructure (AWS/GCP).
  • Strong problem-solving skills and the ability to work in a fast-paced, agile environment.

Required Skills

Python PyTorch TensorFlow LLM Large Language Models MLOps AWS Generative AI Machine Learning Deep Learning RAG Docker Kubernetes

Ready to Take This Challenge?

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