Lead AI Engineer

- A strong track record of designing, building, and shipping AI-powered features that have solved real business problems. - An inquisitive and experimental mindset, with a passion for tackling ambiguous problems that may not have established solutions. - Deep practical knowledge of the modern AI engineering stack (e.g., RAG, vector databases, prompt engineering, fine-tuning techniques, and evaluation frameworks). - The ability to thoughtfully balance cutting-edge research with the practical constraints of production reliability and scalability. - Deep expertise in one or both of the following areas: - Software Engineering & Systems Design: Expertise in Python and experience building scalable services (e.g., using FastAPI, Django). Strong command of system design, infrastructure, and CI/CD pipelines. Ability to take a feature from concept to deployment. - Machine Learning Engineering & Research: A comprehensive background in machine learning, with deep experience in ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and a strong grasp of the underlying theory behind the models and techniques you use. - Bonus Points: - Experience with modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy, LangGraph). - Experience in agentic system design and tool calling (Adk, A2A, MCP, etc.). - Prior work on human-in-the-loop systems, data annotation platforms, or reinforcement learning from human feedback (RLHF). - An interest or experience in synthetic data, human-in-the-loop systems, or AI alignment. - Experience building robust monitoring and observability for AI/ML systems in production.