Job Description
We are building the operating system for the future. Join our elite team of engineers and visionaries as we define the landscape of artificial intelligence in 2026. We are looking for a Senior AI Architect to lead the design and deployment of next-generation Large Language Models and autonomous systems.
In this role, you won't just be writing code; you will be architecting the infrastructure that powers the next decade of human-machine interaction. You will work at the intersection of theoretical AI research and scalable production engineering, ensuring our solutions are not only advanced but robust, ethical, and transformative.
Why Join Us?
- Future-Ready Stack: Work with cutting-edge frameworks and custom-built infrastructure designed for 2026 standards.
- Impactful Work: Your code will directly influence how millions of users interact with AI globally.
- Autonomy: We empower our architects to make high-level decisions and drive technical vision without micromanagement.
Responsibilities
- Architect and design scalable, high-performance AI infrastructure capable of supporting real-time inference at global scale.
- Lead the development and fine-tuning of proprietary Large Language Models (LLMs) using PyTorch and TensorFlow.
- Implement advanced reinforcement learning strategies to optimize model behavior and safety.
- Collaborate with cross-functional teams including product managers, data scientists, and security experts to integrate AI solutions seamlessly.
- Mentor junior engineers and foster a culture of technical excellence and continuous learning.
- Evaluate and integrate emerging AI technologies to keep the company at the forefront of innovation.
Qualifications
- Master's degree or PhD in Computer Science, Machine Learning, or a related field.
- Minimum 5 years of experience in AI engineering, machine learning, or deep learning development.
- Extensive proficiency in Python, C++, and CUDA programming.
- Deep understanding of transformer architectures, attention mechanisms, and NLP best practices.
- Proven experience deploying models in production environments using cloud services (AWS, GCP, or Azure).
- Strong grasp of MLOps, model versioning, and data pipeline management.
- Excellent problem-solving skills and ability to thrive in a fast-paced, ambiguous startup environment.