Job Description
Are you ready to architect the future of intelligence?
Nebula Systems is at the forefront of the 2026 AI revolution, building scalable, ethical, and transformative artificial intelligence solutions. We are seeking a visionary Senior AI Architect to lead our infrastructure strategy and drive the next generation of machine learning capabilities.
In this role, you won't just write code; you will define the architectural backbone for systems that learn, adapt, and evolve. You will bridge the gap between theoretical AI research and production-grade engineering, ensuring our platforms are robust, secure, and ready for the demands of tomorrow.
Why Join Us?
- Work on cutting-edge Large Language Models (LLMs) and Generative AI.
- Competitive equity package and benefits in the heart of Silicon Valley.
- Autonomy to influence technical direction and mentor a world-class team.
If you are passionate about the intersection of deep learning and system design, we want to hear from you.
Responsibilities
- Design and implement scalable AI infrastructure, including MLOps pipelines and distributed computing frameworks.
- Lead the architectural strategy for integrating AI models into real-world enterprise applications.
- Collaborate with cross-functional teams (Data Science, Product, Engineering) to translate business requirements into technical solutions.
- Oversee the performance optimization and cost-efficiency of large-scale training and inference workloads.
- Establish best practices for data governance, model security, and ethical AI deployment.
- Mentor junior engineers and conduct technical code reviews to ensure high standards of quality.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
- Minimum of 8+ years of experience in software engineering, with at least 5 years specifically focused on AI/ML architecture.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of distributed systems, cloud architectures (AWS, GCP, or Azure), and containerization technologies (Docker, Kubernetes).
- Proven experience deploying and managing Large Language Models (LLMs) in production environments.
- Strong background in MLOps tools (MLflow, Airflow) and data orchestration.