Data Scientist

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, LangSmith, or similar) Experience with data pipelines, experiment tracking (MLflow, W&B), and CI/CD ...