Data Scientist

• Design and implement end-to-end evaluation frameworks to assess performance, reliability, and safety of multi-agent AI systems
• Lead experimentation and A/B testing efforts to systematically test hypotheses, validate model improvements, and track performance across agent iterations
• Curate and maintain high-quality ground truth datasets to enable accurate, reproducible evaluation of multi-agent outputs
• Identify and address reliability and accuracy gaps across agent workflows, failure modes, and edge cases in production-like environments
• Stay current on emerging research in agentic AI, LLM evaluation, and multi-agent coordination to continuously improve framework design

Technical Skills

• Proficiency in Python and ML frameworks
• Hands-on experience with LLM APIs and agentic frameworks (LangChain, LlamaIndex, Semetic KernalI)
• Familiarity with evaluation tooling (Ragas, DeepEval, L...