Develops forecasting interfaces, data pipelines, and scheduling systems to predict support contact volume and optimize agent schedules for thousands, incorporating ML models and constraints like labor laws. Requires Python/ML experience and performance focus.
Build and operate the ML platform to productionize models, enable real-time inference, and manage the full ML lifecycle with MLOps best practices. Requires 3+ years platform engineering and 1+ years owning ML systems end-to-end.
Builds forecasting interfaces, data pipelines, and scheduling systems for thousands of support agents using ML models. Requires Python ML libraries experience and background in ML/algorithmic teams with focus on performance optimization.
Builds autonomous AI agents and platform infrastructure for customer support, enhancing LLM performance with RAG techniques, scaling systems with Golang, and integrating STT/TTS. Requires 5+ years software engineering experience with LLMs.
Leads development and deployment of ML models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems. Requires Master's/PhD, production ML experience, deep NLP expertise, Python proficiency, and MLOps knowledge in a fast-paced startup.