Job Description
We are seeking a visionary Senior AI Architect to lead our infrastructure for the 2026 era. At Apex Neural Systems, we are building the foundational neural networks that will power the next decade of autonomous intelligence. You will be responsible for designing scalable, fault-tolerant systems that integrate cutting-edge Machine Learning models with cloud-native architectures.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering, ensuring our platforms are 2026-ready for high-volume data processing and real-time decision making. You will work alongside a world-class team of data scientists, engineers, and product managers to define the technical roadmap for our flagship products.
Why Join Us?
- Work on groundbreaking projects that define the future of AI.
- Competitive compensation package and equity options.
- Flexible remote-first culture with a hub in San Francisco.
- Access to the latest hardware and software stacks.
Responsibilities
- Design and architect scalable AI infrastructure capable of handling petabyte-scale data.
- Lead the development and optimization of Large Language Models (LLMs) and generative AI systems.
- Implement robust CI/CD pipelines for machine learning model deployment and monitoring.
- Ensure system reliability, security, and performance compliance with 2026 industry standards.
- Collaborate with cross-functional teams to translate business requirements into technical solutions.
- Conduct code reviews and mentor junior engineers to foster a culture of technical excellence.
- Research and integrate emerging technologies to maintain a competitive edge.
Qualifications
- Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Minimum of 5+ years of experience in software engineering and machine learning architecture.
- Expert proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Kubernetes, Docker).
- Deep understanding of distributed systems, microservices architecture, and system design.
- Experience with MLOps tools and model lifecycle management.
- Excellent problem-solving skills and ability to thrive in a fast-paced startup environment.