Description

>> Define and implement evaluation strategies and success metrics for AI agents.

>> Assess task completion, correctness, groundedness, hallucination, tool usage, safety, consistency, and overall user experience of AI-driven systems.

>> Build comprehensive test scenarios, including edge and adversarial cases.

>> Develop automated evaluation pipelines leveraging deterministic checks, LLM-as-a-Judge techniques, and custom evaluators.

>> Conduct detailed examination of agent traces, prompts, retrievals, tool calls, and outputs to identify failure modes and performance bottlenecks.

>> Perform regression testing spanning changes in models, prompts, tools, and workflows.

>> Establish quality thresholds, monitoring dashboards, and release gates.

>> Collaborate with AI engineering, product, business, and risk teams for continuous improvement.

Requirements: 

>> Understanding of GenAI, LLMs, RAG, prompts, tool calling, and AI Agents.

>> Proficiency in Python and experience working with APIs, JSON, datasets, and evaluation frameworks.

>> Ability to translate business expectations into measurable evaluation criteria.

>> Experience with AI evaluation / observability tools such as LangSmith, Langfuse, Ragas, DeepEval, MLflow or similar is preferred.

>> Strong analytical, problem-solving, and experimentation mindset

Key Skills

Education

Bachelor's degree