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)