About the Role We’re looking for a hands-on ML Infrastructure Engineer to design, build, and optimize scalable AI systems. You’ll work across the full machine learning lifecycle—from model development and training to deployment and monitoring—enabling real-time and batch AI services at scale. What You’ll Do • Design and optimize ML models, including LLMs and transformer-based architectures • Build distributed training workflows and inference pipelines using PyTorch and AWS • Develop scalable infrastructure for real-time and batch predictions • Lead MLOps platform development including CI/CD, observability, and governance • Collaborate cross-functionally with data, security, and architecture teams Minimum Qualifications • Master’s in Computer Science, ML, or related field • 5+ years of experience building AI/ML systems in cloud environments • Strong background in ML model development, infrastructure, and MLOps • Proficiency with AWS, Python, PyTorch, Docker, Kubernetes, and CI/CD tools • TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM Preferred Qualifications • Experience with LLM fine-tuning, recommendation systems, or real-time inference • Familiarity with feature stores, ML observability, and infrastructure-as-code