Job Description
We are seeking a visionary Project 2026 Lead AI Architect to spearhead the development of our next-generation autonomous systems. At QuantumCore Industries, we are not just building software; we are engineering the foundational intelligence for the future of global logistics and energy management.
In this pivotal role, you will define the architectural blueprint for our flagship 'Project 2026' initiative, bridging the gap between advanced generative models and real-time operational infrastructure. You will work in a high-performance environment where your decisions will have immediate, tangible impacts on millions of users worldwide.
Why Join Us?
- Be at the forefront of the AI revolution with a project that defines the industry standard for 2026 and beyond.
- Competitive compensation package and equity options.
- State-of-the-art development environment and top-tier talent.
Responsibilities
- Design and implement the core neural network architecture for Project 2026, ensuring scalability, latency optimization, and fault tolerance.
- Lead a cross-functional team of ML engineers, data scientists, and backend developers to drive the end-to-end AI lifecycle.
- Collaborate with product managers to translate complex business requirements into technical AI solutions.
- Research and integrate emerging paradigms in reinforcement learning and edge computing to enhance model performance.
- Establish best practices for model deployment, monitoring, and A/B testing within the organization.
Qualifications
- M.S. or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- 10+ years of experience in machine learning engineering, with at least 4 years in a lead or architect role.
- Proven expertise in building and deploying large-scale deep learning models (e.g., Transformers, Graph Neural Networks).
- Strong proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks (e.g., Kubernetes, Apache Spark).
- Deep understanding of MLOps pipelines, cloud infrastructure (AWS/Azure/GCP), and data engineering principles.