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Senior Generative AI Engineer (2026 Vision)

Nexus Future Systems
New York
Estimated Salary
USD 180.000 – USD 260.000
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

Architect the Intelligence of Tomorrow.

Nexus Future Systems is leading the charge into the era of autonomous AI. We are looking for a visionary Senior Generative AI Engineer to define the technical roadmap for our flagship 2026 platform. In this high-impact role, you will build the next generation of Large Language Models (LLMs) and multimodal agents that will redefine human-machine interaction.

As part of our elite R&D team, you will bridge the gap between cutting-edge research and scalable production engineering. You will work in a fast-paced, remote-first environment with top-tier talent from Silicon Valley and NYC.

Responsibilities

  • Model Development: Spearhead the training, fine-tuning, and optimization of proprietary foundation models, including LLMs and diffusion models.
  • RAG & Architecture: Design and implement robust Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and context retention.
  • Production Deployment: Deploy and maintain high-availability AI models using MLOps practices, ensuring low-latency inference and cost-efficiency.
  • Ethical AI: Establish and enforce rigorous safety guidelines, bias mitigation strategies, and alignment protocols for AI outputs.
  • Collaboration: Partner with cross-functional teams (Product, Data Science, Legal) to translate complex AI capabilities into user-centric solutions.

Qualifications

  • Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
  • Experience: 5+ years of professional experience in Deep Learning, NLP, or Generative AI.
  • Programming: Expert proficiency in Python and frameworks such as PyTorch or TensorFlow.
  • Knowledge: Deep understanding of transformer architectures, attention mechanisms, and state-of-the-art LLM techniques.
  • Tools: Proven track record with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS/GCP/Azure).

Required Skills

Python PyTorch TensorFlow Large Language Models (LLMs) NLP Machine Learning Deep Learning MLOps RAG AI Architecture

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