Job Description
We are pioneering the next evolution of artificial intelligence, building the foundational infrastructure for the year 2026. As a Senior AI Architect, you will lead the charge in deploying scalable, high-performance Large Language Models (LLMs) and multimodal systems.
In this high-impact role, you won't just be maintaining systems; you will architect the future of generative AI. We are looking for a visionary engineer who understands the nuances of model optimization, distributed inference, and the architectural shifts required to support the rapid advancements expected in the 2026 era.
Join a team where your code shapes the trajectory of human-machine interaction and define the standard for AI reliability.
Responsibilities
- Architect End-to-End AI Pipelines: Design and implement robust, scalable machine learning infrastructure capable of handling petabyte-scale data and millions of concurrent inference requests.
- Optimize Model Performance: Utilize techniques such as quantization, pruning, and specialized hardware acceleration (NVIDIA GPUs/TPUs) to maximize inference speed and reduce latency.
- Lead MLOps Strategy: Spearhead the deployment of CI/CD pipelines for machine learning models, ensuring automated testing, validation, and seamless rollouts.
- Future-Proof Architecture: Build systems with modularity and adaptability in mind, ensuring our infrastructure remains resilient against evolving model architectures and regulatory changes.
- Cross-Functional Collaboration: Partner with data scientists and product managers to translate complex research into production-ready features that drive user engagement.
Qualifications
- Education: Bachelorās or Masterās degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- Experience: 5+ years of professional experience in software engineering, with at least 2-3 years specifically focused on Machine Learning or AI infrastructure.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, and C++.
- Cloud Mastery: Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Kubernetes, Docker).
- Distributed Systems: Strong understanding of distributed computing principles, message queues (Kafka, RabbitMQ), and high-availability system design.
- Problem Solving: Demonstrated ability to debug complex performance bottlenecks in large-scale systems.