Job Description
Are you ready to define the technological landscape of 2026? Nebula Dynamics is seeking a visionary Senior AI Architect to lead the charge in developing next-generation generative AI solutions.
In this pivotal role, you won't just maintain existing systems; you will architect the infrastructure that powers the future of intelligent automation. We are pushing the boundaries of what's possible with Large Language Models (LLMs), multimodal AI, and autonomous agents. If you are passionate about building systems that scale and evolve, we want to hear from you.
Why join Nebula Dynamics? We offer a competitive salary, a comprehensive equity package, and the opportunity to work on projects that redefine industry standards.
The Role
As our AI Architect, you will bridge the gap between theoretical research and production-grade engineering. You will oversee the lifecycle of our AI models, ensuring they are not only powerful but also efficient, ethical, and secure.
Responsibilities
- Design & Deploy: Architect and implement scalable Generative AI pipelines using state-of-the-art foundation models (e.g., GPT-4, Claude, Llama).
- Optimization: Optimize inference latency and cost efficiency for high-volume, real-time applications.
- RAG Architecture: Design and implement Retrieval-Augmented Generation (RAG) architectures to enhance model accuracy and reduce hallucinations.
- MLOps Leadership: Establish robust MLOps pipelines for continuous model training, retraining, and deployment using CI/CD practices.
- Ethical AI: Define and enforce guidelines for responsible AI, ensuring compliance with emerging data regulations and ethical standards.
- Collaboration: Work closely with product managers, data scientists, and software engineers to translate business needs into technical solutions.
Qualifications
- Experience: 5+ years of experience in Machine Learning, Deep Learning, or AI Engineering.
- Tech Stack: Proficiency in Python, PyTorch, and TensorFlow.
- Domain Expertise: Strong understanding of NLP, LLM fine-tuning, prompt engineering, and Transformer architectures.
- Cloud & DevOps: Experience with major cloud providers (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
- Education: Ph.D. in Computer Science, AI, or a related field, or equivalent practical experience.