Job Description
We are on a mission to architect the digital future. As we approach the pivotal launch of Project 2026, Aether Systems is seeking a visionary Senior AI Architect to lead our core neural infrastructure. You will define the technical roadmap for next-generation generative AI systems, ensuring our solutions are not only scalable and secure but revolutionary.
In this role, you will bridge the gap between theoretical machine learning models and production-grade enterprise software. You will work with a world-class team of engineers, data scientists, and designers to build the AI infrastructure that will define the industry standard for the coming decade.
Why Join Us?
- Work on cutting-edge AI research applied to real-world problems.
- Competitive compensation and equity packages.
- Flexible remote-first culture with state-of-the-art office amenities.
- Focus on personal and professional growth through continuous learning.
Responsibilities
- Lead the architectural design and implementation of the Project 2026 neural network infrastructure, ensuring high availability and fault tolerance.
- Optimize deep learning models for low-latency edge computing environments and massive data throughput.
- Collaborate with product managers and data scientists to translate complex business requirements into scalable technical solutions.
- Establish and enforce best practices for code quality, security, and scalability across the AI engineering team.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Conduct code reviews, performance tuning, and architectural reviews to drive technical debt reduction.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, or a related field, or equivalent practical experience.
- 7+ years of professional experience in Machine Learning, Deep Learning, and Software Engineering.
- Expert proficiency in Python, PyTorch, or TensorFlow, with hands-on experience in model deployment.
- Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Kubernetes/Docker).
- Experience with MLOps tools and pipelines (e.g., MLflow, Kubeflow, Airflow).
- Demonstrated ability to lead technical initiatives and mentor engineering teams.