Senior AI Evaluation Engineer - Agentic Systems

Dayforce

Toronto

Hybrid

CAD 120,000 - 180,000

Full time

3 days ago
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Job summary

Dayforce is seeking an AI Engineer - Agentic Systems Evaluation to help define how we measure, test, and improve the quality of enterprise AI systems. You will design evaluation frameworks for agentic AI, multi-agent workflows, and RAG-driven systems, tackling reliability, cost, and safety.

Join a team building scalable evaluation infrastructure and producing rigorous benchmarks to guide production deployments.

Qualifications

  • Experience with Python and software engineering.
  • LLM application development experience.
  • AI/ML evaluation and experimentation.
  • Automated testing and data analysis experience.
  • Familiarity with RAG, embeddings, hybrid search, and reranking.
  • Experience with tool/function calling and structured outputs.
  • Agent orchestration and multi-agent workflows.
  • AI observability and tracing.
  • APIs and enterprise integrations.

Responsibilities

  • Build Agentic Evaluation Frameworks covering the full AI execution lifecycle.
  • Design experiments and benchmarks for architecture comparisons (single-agent vs multi-agent).
  • Develop automated evaluation and regression testing infrastructure.
  • Analyze agent traces, tool usage, and failure modes to improve reliability and cost.

Skills

Python
LLM development
AI evaluation
Automated testing
RAG and embeddings
Tool calls and structured outputs
Agent orchestration
AI observability
APIs and enterprise integrations

Education

Bachelor's degree in CS/Engineering

Tools

Agents SDK
LangGraph/LangChain

Job description

Dayforce is seeking an AI Engineer - Agentic Systems Evaluation to help define how we measure, test, and improve the quality of enterprise AI systems. You will design evaluation frameworks for agentic AI, multi-agent workflows, and RAG-driven systems, tackling reliability, cost, and safety.

Join a team building scalable evaluation infrastructure and producing rigorous benchmarks to guide production deployments.

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