Job Description
We are seeking a visionary Future-Ready AI Architect to lead our infrastructure for the 2026 roadmap. At Apex Digital Systems, we are not just building software; we are engineering the intelligence of tomorrow. If you possess a deep understanding of generative models, large-scale distributed systems, and next-gen AI infrastructure, this is your opportunity to shape the future of technology.
In this pivotal role, you will bridge the gap between cutting-edge research and scalable production engineering. You will design resilient architectures capable of handling petabyte-scale data, optimize model inference at the edge, and implement MLOps strategies that ensure our AI systems remain robust, secure, and efficient.
Why Join Us?
- Work on next-generation Generative AI infrastructure.
- Competitive compensation and equity packages.
- Flexible remote-first culture with premium San Francisco perks.
- Access to the latest hardware and cloud resources.
Responsibilities
- Design and architect scalable, high-performance AI infrastructure solutions aligned with the 2026 technology roadmap.
- Implement and optimize MLOps pipelines to streamline model training, deployment, and monitoring processes.
- Collaborate with research teams to translate theoretical models into production-grade software.
- Ensure system reliability, security, and scalability across cloud and on-premise environments.
- Drive technical innovation in areas such as edge computing, distributed inference, and quantum-ready data structures.
- Lead code reviews and mentor junior engineers in best practices for AI engineering.
Qualifications
- 5+ years of experience in software engineering with a focus on Machine Learning and Artificial Intelligence.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Experience designing distributed systems for high availability and low latency.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, agile environment.
- Experience with LLMOps and RAG (Retrieval-Augmented Generation) architectures.