The role involves leading the design and delivery of production-grade machine learning and generative AI systems to address complex business problems. Responsibilities include managing architecture and implementation across data pipelines, feature stores, training workflows, and model-serving infrastructure. The position requires driving advanced use cases such as deep learning, NLP, recommendation systems, and LLM applications including RAG. The individual will guide technical direction on model selection, distributed training, and responsible AI controls. Additionally, the role entails mentoring engineers and establishing reusable patterns and platform capabilities to ensure high performance and reliability.

Candidates must possess 7-9 years of experience in software engineering and machine learning with a proven track record of delivering production-scale AI systems. Expert-level programming skills in Python are required, alongside strong software engineering expertise in Java or Go. Applicants need extensive experience in MLOps, platform engineering, and building generative AI applications using RAG pipelines and vector databases. The role requires a strong architectural understanding of distributed systems and cloud-native platforms, though no specific degree is explicitly mentioned.